Responsibilities (Text Only) You'll work on high-impact, technically ambitious projects that directly shape the future of Microsoft 365 Copilot.Examples include: - Advancing deep reasoning in Microsoft 365 Copilot by applying next-generation LLM fine-tuning and reinforcement learning techniques. - Improving Copilot Chat and Researcher response quality through state-of-the-art grounding data selection strategies. - Enhancing Copilot Search by developing novel content representation models. - Building the next wave of recommendation and personalization capabilities across M365 Copilot experiences. - In addition to driving innovation, you'll help grow the team's technical depth by mentoring and developing talent. Qualifications (Text Only) Required Qualifications: - A Ph.D. in Computer Science, Math, Physics, Statistics, OR related areas is highly preferred. Candidates with master's degree with proven industry experience or a strong publication record in the areas of Information Retrieval, Machine Learning, Natural Language Processing, and Deep Learning are considered as well. - Extensive hands-on experience building and deploying products using Machine Learning. Specifically, we are looking for expertise in Natural Language Processing, Large Language Models, Information Retrieval, and Recommendation Systems with a good understanding of techniques like Differential Privacy, Responsible AI and related areas. - High proficiency in deploying machine learning applications at scale in real production environments and proven track record of successfully shipping applied research to production is a must - Excellent problem solving and data analysis skills and a good grasp of applied statistics. Particularly, expertise in developing or applying predictive analytics, statistical modelling, data mining, or machine learning algorithms, especially at scale - Strong people leadership skills to influence others, with the ability to tech-lead, understand team dynamics, retain, attract, and develop team members. - Grounded in growth mindset, and advocate for diversity and inclusion. - Customer obsession and passionate about product impact - Excellent verbal and written communication skills, with the ability to simplify and explain complex ideas. - Effective collaboration skills while working effectively within a globally distributed organization. Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: - Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter. Preferred Qualifications: - PhD degree in Computer Science, Statistics, Mathematics, OR a related field. - Proven track record in training large language models and post-training large language models, using reinforcement learning or similar techniques - First-hand experience building agentic AI models using deep learning Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form. Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.
Jul 17, 2025
Full time
Responsibilities (Text Only) You'll work on high-impact, technically ambitious projects that directly shape the future of Microsoft 365 Copilot.Examples include: - Advancing deep reasoning in Microsoft 365 Copilot by applying next-generation LLM fine-tuning and reinforcement learning techniques. - Improving Copilot Chat and Researcher response quality through state-of-the-art grounding data selection strategies. - Enhancing Copilot Search by developing novel content representation models. - Building the next wave of recommendation and personalization capabilities across M365 Copilot experiences. - In addition to driving innovation, you'll help grow the team's technical depth by mentoring and developing talent. Qualifications (Text Only) Required Qualifications: - A Ph.D. in Computer Science, Math, Physics, Statistics, OR related areas is highly preferred. Candidates with master's degree with proven industry experience or a strong publication record in the areas of Information Retrieval, Machine Learning, Natural Language Processing, and Deep Learning are considered as well. - Extensive hands-on experience building and deploying products using Machine Learning. Specifically, we are looking for expertise in Natural Language Processing, Large Language Models, Information Retrieval, and Recommendation Systems with a good understanding of techniques like Differential Privacy, Responsible AI and related areas. - High proficiency in deploying machine learning applications at scale in real production environments and proven track record of successfully shipping applied research to production is a must - Excellent problem solving and data analysis skills and a good grasp of applied statistics. Particularly, expertise in developing or applying predictive analytics, statistical modelling, data mining, or machine learning algorithms, especially at scale - Strong people leadership skills to influence others, with the ability to tech-lead, understand team dynamics, retain, attract, and develop team members. - Grounded in growth mindset, and advocate for diversity and inclusion. - Customer obsession and passionate about product impact - Excellent verbal and written communication skills, with the ability to simplify and explain complex ideas. - Effective collaboration skills while working effectively within a globally distributed organization. Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: - Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter. Preferred Qualifications: - PhD degree in Computer Science, Statistics, Mathematics, OR a related field. - Proven track record in training large language models and post-training large language models, using reinforcement learning or similar techniques - First-hand experience building agentic AI models using deep learning Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form. Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.
Machine Learning Engineer, Amazon Music Search As a machine learning engineer on the Amazon Music Search team, you will collaborate with scientists on developing and evaluating machine learning models (Search Relevancy & Ranking) using large datasets such as meta-data and search queries to improve the customer experience through better search results or song sequencing. You will own scaling up successful prototypes and implementing a reliable automated production workflow for the model. You will collaborate with software development engineers to integrate the model with the customer experience. We are looking for individuals with a passion for learning, researching, and deploying production-ready science solutions in a highly collaborative environment. We like to ideate, experiment, iterate, optimize, and scale quickly while thoughtfully balancing speed and quality. Key Job Responsibilities Use ML and Generative AI tools, such as Amazon SageMaker and Amazon Bedrock, to provide scalable production solutions to improve the customer experience, label data, build, train, tune, and deploy models. Collaborate with our Applied scientists to create and fine-tune scalable ML and Generative AI solutions for business problems. Interact with product stakeholders directly to understand the business problem and aid them in the implementation of their ML ecosystem. Analyze and extract relevant information from large amounts of historical data to help automate and optimize key processes. Work closely with science and engineering teams to drive model implementations and develop new algorithms. About the Team Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Learn more at Amazon Music . BASIC QUALIFICATIONS 3+ years of non-internship professional software development experience. 2+ years of non-internship design or architecture (design patterns, reliability, and scaling) of new and existing systems experience. Experience programming with at least one software programming language. Bachelor's degree in computer science or equivalent. PREFERRED QUALIFICATIONS 2+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization, or search experience. Experience related to AWS services such as SageMaker, EMR, S3, DynamoDB, EC2, etc. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit here for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner. Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Feb 11, 2025
Full time
Machine Learning Engineer, Amazon Music Search As a machine learning engineer on the Amazon Music Search team, you will collaborate with scientists on developing and evaluating machine learning models (Search Relevancy & Ranking) using large datasets such as meta-data and search queries to improve the customer experience through better search results or song sequencing. You will own scaling up successful prototypes and implementing a reliable automated production workflow for the model. You will collaborate with software development engineers to integrate the model with the customer experience. We are looking for individuals with a passion for learning, researching, and deploying production-ready science solutions in a highly collaborative environment. We like to ideate, experiment, iterate, optimize, and scale quickly while thoughtfully balancing speed and quality. Key Job Responsibilities Use ML and Generative AI tools, such as Amazon SageMaker and Amazon Bedrock, to provide scalable production solutions to improve the customer experience, label data, build, train, tune, and deploy models. Collaborate with our Applied scientists to create and fine-tune scalable ML and Generative AI solutions for business problems. Interact with product stakeholders directly to understand the business problem and aid them in the implementation of their ML ecosystem. Analyze and extract relevant information from large amounts of historical data to help automate and optimize key processes. Work closely with science and engineering teams to drive model implementations and develop new algorithms. About the Team Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Learn more at Amazon Music . BASIC QUALIFICATIONS 3+ years of non-internship professional software development experience. 2+ years of non-internship design or architecture (design patterns, reliability, and scaling) of new and existing systems experience. Experience programming with at least one software programming language. Bachelor's degree in computer science or equivalent. PREFERRED QUALIFICATIONS 2+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization, or search experience. Experience related to AWS services such as SageMaker, EMR, S3, DynamoDB, EC2, etc. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit here for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner. Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Grow with us. We are looking for a Machine Learning Engineer to work along the end-to-end ML lifecycle, alongside our existing Product & Engineering team. About Trudenty: The Trudenty Trust Network provides personalised consumer fraud risk intelligence for fraud prevention across the commerce and payments ecosystem, starting with first-party and APP fraud prevention. We are at an exciting point in our journey, as we go to market and drive growth of The Trudenty Trust Network. This next chapter of our story is one in which we will drive impact across the commerce ecosystem, create to stay at the leading edge of innovation across the industry whilst building material value for our team (inclusive of shareholders). We are a 10 person seed stage company that has secured partnerships with notable names in the payments and commerce ecosystem, and raised investment from our first choice of partners who align with our values and ambition for the future. Our team is one of exceptional 'outliers'; defined by grit, resilience, creativity in problem solving, intelligence and mastery of our domains. We are also mission-driven and results-oriented. Working with us, you will get the opportunity to do some of the best work of your life and unfold your full potential as a human. We are a remote team, that co-works from London frequently. So easy travel into London should be possible for everyone in our team. The role: We are looking for a Machine Learning Engineer with a spike in data engineering and maintaining real-time data pipelines. You will work with our Product & Engineering team along the end-to-end algorithm lifecycle to advance the Trudenty Trust Network. A bit more on what you'll do: Data Engineering: Develop and maintain real-time data pipelines for processing large-scale data. Ensure data quality and integrity in all stages of the data lifecycle. Develop and maintain ETL processes for data ingestion and processing. Algorithm Development, Model Training and Optimisation: Design, develop, and implement advanced machine learning algorithms for fraud prevention and user personalization. Train and fine-tune machine learning models using relevant datasets to achieve optimal performance. Implement strategies for continuous model improvement and optimization. Data Mining & Analysis: Apply data mining techniques such as clustering, classification, regression, and anomaly detection to discover patterns and trends in large datasets. Analyze and preprocess large datasets to extract meaningful insights and features for model training. MLOps - Deployment into production environments, Monitoring and Maintenance: Experience deploying and maintaining large-scale ML inference pipelines into production. Implement and monitor model performance in production environments on Kubernetes and AWS cloud platforms. Utilize Docker for containerization and orchestrate containerized applications using Kubernetes. Code Review and Documentation: Conduct code reviews to ensure high-quality, scalable, and maintainable code. Create comprehensive documentation for developed algorithms and models. Collaboration: Collaborate with our cross-functional team; including the founders, sales, data scientists, engineers, and product to understand business requirements and implement effective solutions. Research and Innovation: Stay abreast of the latest advancements in fraud prevention and machine learning and contribute to the exploration and integration of innovative techniques. About you: You will have proven experience with data science and a track record of implementing fraud prevention, credit scoring or personalization algorithms. Setting up and maintaining real data pipelines to feed your ML models is light work for you, and you would have been as comfortable if this JD was for a 'data engineer'. You have worked in a high growth and fast moving company. You are agile, comfortable with ambiguity and are a creative thinker who can apply research and past experiences to new problems. What we're looking for: Education & Experience: Bachelor's or Master's degree in Computer Science, Data Science, or a related field. 5+ years of professional experience in a relevant area like fraud prevention or credit scoring. Machine Learning Expertise: Strong understanding of machine learning algorithms and their practical applications, particularly in fraud prevention and user personalization. Experience designing, developing, and implementing advanced machine learning models. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. Data Engineering Skills: Proficiency in developing and maintaining real-time data pipelines for processing large-scale data. Experience with ETL processes for data ingestion and processing. Proficiency in Python and SQL. Experience with big data technologies like Apache Hadoop and Apache Spark. Familiarity with real-time data processing frameworks such as Apache Kafka or Flink. MLOps & Deployment: Experience deploying and maintaining large-scale ML inference pipelines into production environments. Proficiency with Docker for containerization and Kubernetes for orchestration. Familiarity with AWS cloud platform (experience with GCP or Azure is a plus). Experience monitoring and optimizing model performance in production settings. Programming Languages: Strong coding skills in Python and SQL. Experience with Node.js, JavaScript (JS), and TypeScript (TS) is a plus. Statistical Knowledge: Solid understanding of statistical concepts and methodologies for analyzing and interpreting large datasets. Ability to apply statistical techniques to validate models and algorithms. Data Manipulation & Analysis: Proficient in data manipulation and analysis using tools like Pandas, NumPy, and Jupyter Notebooks. Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau to communicate insights effectively. Our offer: Cash: Depends on experience. Equity: Generous equity package, on a standard vesting schedule. Impact & Exposure: Work at the leading edge of innovation building our machine-learning powered smart contracts for fraud prevention. Growth: An opportunity to wear many hats, and grow into a role you can inform. Hybrid work: Flexibility to work from home, with travel into London. The process: Submit your CV along with answers to the handful of questions we ask of every candidate. A 60min call to explore initial fit with the founders. A 60min technical problem solving interview, alongside your potential ML colleague. Final discussion with the Founder CEO to align before we make a formal offer.
Feb 08, 2025
Full time
Grow with us. We are looking for a Machine Learning Engineer to work along the end-to-end ML lifecycle, alongside our existing Product & Engineering team. About Trudenty: The Trudenty Trust Network provides personalised consumer fraud risk intelligence for fraud prevention across the commerce and payments ecosystem, starting with first-party and APP fraud prevention. We are at an exciting point in our journey, as we go to market and drive growth of The Trudenty Trust Network. This next chapter of our story is one in which we will drive impact across the commerce ecosystem, create to stay at the leading edge of innovation across the industry whilst building material value for our team (inclusive of shareholders). We are a 10 person seed stage company that has secured partnerships with notable names in the payments and commerce ecosystem, and raised investment from our first choice of partners who align with our values and ambition for the future. Our team is one of exceptional 'outliers'; defined by grit, resilience, creativity in problem solving, intelligence and mastery of our domains. We are also mission-driven and results-oriented. Working with us, you will get the opportunity to do some of the best work of your life and unfold your full potential as a human. We are a remote team, that co-works from London frequently. So easy travel into London should be possible for everyone in our team. The role: We are looking for a Machine Learning Engineer with a spike in data engineering and maintaining real-time data pipelines. You will work with our Product & Engineering team along the end-to-end algorithm lifecycle to advance the Trudenty Trust Network. A bit more on what you'll do: Data Engineering: Develop and maintain real-time data pipelines for processing large-scale data. Ensure data quality and integrity in all stages of the data lifecycle. Develop and maintain ETL processes for data ingestion and processing. Algorithm Development, Model Training and Optimisation: Design, develop, and implement advanced machine learning algorithms for fraud prevention and user personalization. Train and fine-tune machine learning models using relevant datasets to achieve optimal performance. Implement strategies for continuous model improvement and optimization. Data Mining & Analysis: Apply data mining techniques such as clustering, classification, regression, and anomaly detection to discover patterns and trends in large datasets. Analyze and preprocess large datasets to extract meaningful insights and features for model training. MLOps - Deployment into production environments, Monitoring and Maintenance: Experience deploying and maintaining large-scale ML inference pipelines into production. Implement and monitor model performance in production environments on Kubernetes and AWS cloud platforms. Utilize Docker for containerization and orchestrate containerized applications using Kubernetes. Code Review and Documentation: Conduct code reviews to ensure high-quality, scalable, and maintainable code. Create comprehensive documentation for developed algorithms and models. Collaboration: Collaborate with our cross-functional team; including the founders, sales, data scientists, engineers, and product to understand business requirements and implement effective solutions. Research and Innovation: Stay abreast of the latest advancements in fraud prevention and machine learning and contribute to the exploration and integration of innovative techniques. About you: You will have proven experience with data science and a track record of implementing fraud prevention, credit scoring or personalization algorithms. Setting up and maintaining real data pipelines to feed your ML models is light work for you, and you would have been as comfortable if this JD was for a 'data engineer'. You have worked in a high growth and fast moving company. You are agile, comfortable with ambiguity and are a creative thinker who can apply research and past experiences to new problems. What we're looking for: Education & Experience: Bachelor's or Master's degree in Computer Science, Data Science, or a related field. 5+ years of professional experience in a relevant area like fraud prevention or credit scoring. Machine Learning Expertise: Strong understanding of machine learning algorithms and their practical applications, particularly in fraud prevention and user personalization. Experience designing, developing, and implementing advanced machine learning models. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. Data Engineering Skills: Proficiency in developing and maintaining real-time data pipelines for processing large-scale data. Experience with ETL processes for data ingestion and processing. Proficiency in Python and SQL. Experience with big data technologies like Apache Hadoop and Apache Spark. Familiarity with real-time data processing frameworks such as Apache Kafka or Flink. MLOps & Deployment: Experience deploying and maintaining large-scale ML inference pipelines into production environments. Proficiency with Docker for containerization and Kubernetes for orchestration. Familiarity with AWS cloud platform (experience with GCP or Azure is a plus). Experience monitoring and optimizing model performance in production settings. Programming Languages: Strong coding skills in Python and SQL. Experience with Node.js, JavaScript (JS), and TypeScript (TS) is a plus. Statistical Knowledge: Solid understanding of statistical concepts and methodologies for analyzing and interpreting large datasets. Ability to apply statistical techniques to validate models and algorithms. Data Manipulation & Analysis: Proficient in data manipulation and analysis using tools like Pandas, NumPy, and Jupyter Notebooks. Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau to communicate insights effectively. Our offer: Cash: Depends on experience. Equity: Generous equity package, on a standard vesting schedule. Impact & Exposure: Work at the leading edge of innovation building our machine-learning powered smart contracts for fraud prevention. Growth: An opportunity to wear many hats, and grow into a role you can inform. Hybrid work: Flexibility to work from home, with travel into London. The process: Submit your CV along with answers to the handful of questions we ask of every candidate. A 60min call to explore initial fit with the founders. A 60min technical problem solving interview, alongside your potential ML colleague. Final discussion with the Founder CEO to align before we make a formal offer.
Grow with us. We are looking for a Machine Learning Engineer to work along the end-to-end ML lifecycle, alongside our existing Product & Engineering team. About Trudenty: The Trudenty Trust Network provides personalised consumer fraud risk intelligence for fraud prevention across the commerce and payments ecosystem, starting with first-party and APP fraud prevention. We are at an exciting point in our journey, as we go to market and drive growth of The Trudenty Trust Network. This next chapter of our story is one in which we will drive impact across the commerce ecosystem, create to stay at the leading edge of innovation across the industry whilst building material value for our team (inclusive of shareholders). We are a 10 person seed stage company that has secured partnerships with notable names in the payments and commerce ecosystem, and raised investment from our first choice of partners who align with our values and ambition for the future. Our team is one of exceptional 'outliers'; defined by grit, resilience, creativity in problem solving, intelligence and mastery of our domains. We are also mission-driven and results-oriented. Working with us, you will get the opportunity to do some of the best work of your life and unfold your full potential as a human. We are a remote team, that co-works from London frequently. So easy travel into London should be possible for everyone in our team. The role We are looking for a Machine Learning Engineer with a spike in data engineering and maintaining real-time data pipelines. You will work with our Product & Engineering team along the end-to-end algorithm lifecycle to advance the Trudenty Trust Network. A bit more on what you'll do: Data Engineering Develop and maintain real-time data pipelines for processing large-scale data. Ensure data quality and integrity in all stages of the data lifecycle. Develop and maintain ETL processes for data ingestion and processing. Algorithm Development, Model Training and Optimisation Design, develop, and implement advanced machine learning algorithms for fraud prevention and user personalization. Train and fine-tune machine learning models using relevant datasets to achieve optimal performance. Implement strategies for continuous model improvement and optimization. Data Mining & Analysis Apply data mining techniques such as clustering, classification, regression, and anomaly detection to discover patterns and trends in large datasets. Analyze and preprocess large datasets to extract meaningful insights and features for model training. MLOps - Deployment into production environments, Monitoring and Maintenance Experience deploying and maintaining large-scale ML inference pipelines into production. Implement and monitor model performance in production environments on Kubernetes and AWS cloud platforms. Utilize Docker for containerization and orchestrate containerized applications using Kubernetes. Code Review and Documentation Conduct code reviews to ensure high-quality, scalable, and maintainable code. Create comprehensive documentation for developed algorithms and models. Collaboration Collaborate with our cross-functional team; including the founders, sales, data scientists, engineers, and product to understand business requirements and implement effective solutions. Research and Innovation Stay abreast of the latest advancements in fraud prevention and machine learning and contribute to the exploration and integration of innovative techniques. About you: You will have proven experience with data science and a track record of implementing fraud prevention, credit scoring or personalization algorithms. Setting up and maintaining real data pipelines to feed your ML models is light work for you, and you would have been as comfortable if this JD was for a 'data engineer'. You have worked in a high growth and fast moving company. You are agile, comfortable with ambiguity and are a creative thinker who can apply research and past experiences to new problems. What we're looking for: Education & Experience: Bachelor's or Master's degree in Computer Science, Data Science, or a related field. 5+ years of professional experience in a relevant area like fraud prevention or credit scoring. Machine Learning Expertise: Strong understanding of machine learning algorithms and their practical applications, particularly in fraud prevention and user personalization. Experience designing, developing, and implementing advanced machine learning models. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. Data Engineering Skills: Proficiency in developing and maintaining real-time data pipelines for processing large-scale data. Experience with ETL processes for data ingestion and processing. Proficiency in Python and SQL. Experience with big data technologies like Apache Hadoop and Apache Spark. Familiarity with real-time data processing frameworks such as Apache Kafka or Flink. MLOps & Deployment: Experience deploying and maintaining large-scale ML inference pipelines into production environments. Proficiency with Docker for containerization and Kubernetes for orchestration. Familiarity with AWS cloud platform (experience with GCP or Azure is a plus). Experience monitoring and optimizing model performance in production settings. Programming Languages: Strong coding skills in Python and SQL. Experience with Node.js, JavaScript (JS), and TypeScript (TS) is a plus. Statistical Knowledge: Solid understanding of statistical concepts and methodologies for analyzing and interpreting large datasets. Ability to apply statistical techniques to validate models and algorithms. Data Manipulation & Analysis: Proficient in data manipulation and analysis using tools like Pandas, NumPy, and Jupyter Notebooks. Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau to communicate insights effectively. Our offer: Cash: Depends on experience. Equity: Generous equity package, on a standard vesting schedule. Impact & Exposure: Work at the leading edge of innovation building our machine-learning powered smart contracts for fraud prevention. Growth: An opportunity to wear many hats, and grow into a role you can inform. Hybrid work: Flexibility to work from home, with travel into London. The process: Submit your CV along with answers to the handful of questions we ask of every candidate. A 60min call to explore initial fit with the founders. A 60min technical problem solving interview, alongside your potential ML colleague. Final discussion with the Founder CEO to align before we make a formal offer.
Feb 07, 2025
Full time
Grow with us. We are looking for a Machine Learning Engineer to work along the end-to-end ML lifecycle, alongside our existing Product & Engineering team. About Trudenty: The Trudenty Trust Network provides personalised consumer fraud risk intelligence for fraud prevention across the commerce and payments ecosystem, starting with first-party and APP fraud prevention. We are at an exciting point in our journey, as we go to market and drive growth of The Trudenty Trust Network. This next chapter of our story is one in which we will drive impact across the commerce ecosystem, create to stay at the leading edge of innovation across the industry whilst building material value for our team (inclusive of shareholders). We are a 10 person seed stage company that has secured partnerships with notable names in the payments and commerce ecosystem, and raised investment from our first choice of partners who align with our values and ambition for the future. Our team is one of exceptional 'outliers'; defined by grit, resilience, creativity in problem solving, intelligence and mastery of our domains. We are also mission-driven and results-oriented. Working with us, you will get the opportunity to do some of the best work of your life and unfold your full potential as a human. We are a remote team, that co-works from London frequently. So easy travel into London should be possible for everyone in our team. The role We are looking for a Machine Learning Engineer with a spike in data engineering and maintaining real-time data pipelines. You will work with our Product & Engineering team along the end-to-end algorithm lifecycle to advance the Trudenty Trust Network. A bit more on what you'll do: Data Engineering Develop and maintain real-time data pipelines for processing large-scale data. Ensure data quality and integrity in all stages of the data lifecycle. Develop and maintain ETL processes for data ingestion and processing. Algorithm Development, Model Training and Optimisation Design, develop, and implement advanced machine learning algorithms for fraud prevention and user personalization. Train and fine-tune machine learning models using relevant datasets to achieve optimal performance. Implement strategies for continuous model improvement and optimization. Data Mining & Analysis Apply data mining techniques such as clustering, classification, regression, and anomaly detection to discover patterns and trends in large datasets. Analyze and preprocess large datasets to extract meaningful insights and features for model training. MLOps - Deployment into production environments, Monitoring and Maintenance Experience deploying and maintaining large-scale ML inference pipelines into production. Implement and monitor model performance in production environments on Kubernetes and AWS cloud platforms. Utilize Docker for containerization and orchestrate containerized applications using Kubernetes. Code Review and Documentation Conduct code reviews to ensure high-quality, scalable, and maintainable code. Create comprehensive documentation for developed algorithms and models. Collaboration Collaborate with our cross-functional team; including the founders, sales, data scientists, engineers, and product to understand business requirements and implement effective solutions. Research and Innovation Stay abreast of the latest advancements in fraud prevention and machine learning and contribute to the exploration and integration of innovative techniques. About you: You will have proven experience with data science and a track record of implementing fraud prevention, credit scoring or personalization algorithms. Setting up and maintaining real data pipelines to feed your ML models is light work for you, and you would have been as comfortable if this JD was for a 'data engineer'. You have worked in a high growth and fast moving company. You are agile, comfortable with ambiguity and are a creative thinker who can apply research and past experiences to new problems. What we're looking for: Education & Experience: Bachelor's or Master's degree in Computer Science, Data Science, or a related field. 5+ years of professional experience in a relevant area like fraud prevention or credit scoring. Machine Learning Expertise: Strong understanding of machine learning algorithms and their practical applications, particularly in fraud prevention and user personalization. Experience designing, developing, and implementing advanced machine learning models. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. Data Engineering Skills: Proficiency in developing and maintaining real-time data pipelines for processing large-scale data. Experience with ETL processes for data ingestion and processing. Proficiency in Python and SQL. Experience with big data technologies like Apache Hadoop and Apache Spark. Familiarity with real-time data processing frameworks such as Apache Kafka or Flink. MLOps & Deployment: Experience deploying and maintaining large-scale ML inference pipelines into production environments. Proficiency with Docker for containerization and Kubernetes for orchestration. Familiarity with AWS cloud platform (experience with GCP or Azure is a plus). Experience monitoring and optimizing model performance in production settings. Programming Languages: Strong coding skills in Python and SQL. Experience with Node.js, JavaScript (JS), and TypeScript (TS) is a plus. Statistical Knowledge: Solid understanding of statistical concepts and methodologies for analyzing and interpreting large datasets. Ability to apply statistical techniques to validate models and algorithms. Data Manipulation & Analysis: Proficient in data manipulation and analysis using tools like Pandas, NumPy, and Jupyter Notebooks. Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau to communicate insights effectively. Our offer: Cash: Depends on experience. Equity: Generous equity package, on a standard vesting schedule. Impact & Exposure: Work at the leading edge of innovation building our machine-learning powered smart contracts for fraud prevention. Growth: An opportunity to wear many hats, and grow into a role you can inform. Hybrid work: Flexibility to work from home, with travel into London. The process: Submit your CV along with answers to the handful of questions we ask of every candidate. A 60min call to explore initial fit with the founders. A 60min technical problem solving interview, alongside your potential ML colleague. Final discussion with the Founder CEO to align before we make a formal offer.
NLP / LLM Scientist - Applied AI ML Senior Associate - Machine Learning Centre of Excellence The Machine Learning Center of Excellence invites the successful candidate to apply sophisticated machine learning methods to a wide variety of complex tasks including natural language processing, speech analytics, time series, reinforcement learning and recommendation systems. The candidate must excel in working in a highly collaborative environment together with the business, technologists and control partners to deploy solutions into production. The candidate must also have a strong passion for machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field. The candidate must have practiced expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated. Job Responsibilities Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community. Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as NLP, speech recognition and analytics, time-series predictions or recommendation systems. Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production. Drive Firm wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business. Required Qualifications, Capabilities, and Skills Solid background in NLP or speech recognition and analytics, personalization/recommendation and hands-on experience and solid understanding of machine learning and deep learning methods. PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science with reasonable industry experience, or an MS with industry or research experience in the field. Applied experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas). Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals. Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences. Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments. Curious, hardworking and detail-oriented, and motivated by complex analytical problems. Preferred Qualifications, Capabilities, and Skills Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development. Knowledge in search/ranking, Reinforcement Learning or Meta Learning. Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality code. Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal. About MLCOE The Machine Learning Center of Excellence (MCLOE) team partners across the firm to create and share Machine Learning Solutions for our most challenging business problems. In this role you will work and collaborate with a team comprised of a multi-disciplinary community of experts focused exclusively on Machine Learning. On this team you will work with cutting-edge techniques in disciplines such as Deep Learning and Reinforcement Learning. For more information about the MLCOE, please visit . To learn about how we're using AI/ML to drive transformational change, please read this blog: . The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm's data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm's commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly. Company: Chase- Candidate Experience page Level of Experience: Senior (5+ years of experience) Tagged as: Big Data , Industry , Natural Language Processing , NLP , Speech Recognition , United Kingdom
Jan 28, 2025
Full time
NLP / LLM Scientist - Applied AI ML Senior Associate - Machine Learning Centre of Excellence The Machine Learning Center of Excellence invites the successful candidate to apply sophisticated machine learning methods to a wide variety of complex tasks including natural language processing, speech analytics, time series, reinforcement learning and recommendation systems. The candidate must excel in working in a highly collaborative environment together with the business, technologists and control partners to deploy solutions into production. The candidate must also have a strong passion for machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field. The candidate must have practiced expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated. Job Responsibilities Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community. Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as NLP, speech recognition and analytics, time-series predictions or recommendation systems. Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production. Drive Firm wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business. Required Qualifications, Capabilities, and Skills Solid background in NLP or speech recognition and analytics, personalization/recommendation and hands-on experience and solid understanding of machine learning and deep learning methods. PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science with reasonable industry experience, or an MS with industry or research experience in the field. Applied experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas). Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals. Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences. Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments. Curious, hardworking and detail-oriented, and motivated by complex analytical problems. Preferred Qualifications, Capabilities, and Skills Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development. Knowledge in search/ranking, Reinforcement Learning or Meta Learning. Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality code. Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal. About MLCOE The Machine Learning Center of Excellence (MCLOE) team partners across the firm to create and share Machine Learning Solutions for our most challenging business problems. In this role you will work and collaborate with a team comprised of a multi-disciplinary community of experts focused exclusively on Machine Learning. On this team you will work with cutting-edge techniques in disciplines such as Deep Learning and Reinforcement Learning. For more information about the MLCOE, please visit . To learn about how we're using AI/ML to drive transformational change, please read this blog: . The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm's data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm's commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly. Company: Chase- Candidate Experience page Level of Experience: Senior (5+ years of experience) Tagged as: Big Data , Industry , Natural Language Processing , NLP , Speech Recognition , United Kingdom
