Senior Research Scientist (LLM post training) United Kingdom PolyAI automates customer service through lifelike voice assistants that let customers lead a conversation. Our voice assistants make it possible for businesses to deliver outstanding customer service that rivals their human agents. Our customers, which include the world's leading logos, are expanding how they use our platform, driving automation of critical customer service operations and integrating PolyAI into their daily customer service workflows. We are looking for a Senior Research Scientist to join our world-class team and lead cutting-edge work on large language model (LLM) post-training. This is not just about applying standard fine-tuning techniques - it's about building the future of dialogue systems with novel approaches to reasoning, reinforcement learning, audio-first LLMs, and more. As a Senior Research Scientist at PolyAI, you'll lead impactful research projects from ideation through to deployment. You'll be driving innovation in how we train and adapt LLMs for real-world conversations - spanning voice, text, and multimodal contexts. You'll work on frontier techniques such as: Conversational reinforcement learning Streaming and continuous turn-taking Audio-native LLMs Distillation of reasoning models Long-context You'll also play a key role in shaping the scientific direction of our research, mentoring junior colleagues, and collaborating cross-functionally to bring research into production. Responsibilities: Lead and execute complex research projects with clear business impact. Design and implement novel post-training strategies including preference tuning, reward modeling, and synthetic supervision. Develop innovative model architectures and training approaches for conversational AI, including speech-aware and multimodal models. Conduct empirical studies to assess model performance in live deployments and iterate quickly based on real-world data. Generate, collect, and annotate training data - including synthetic and real-world conversational datasets - with an eye for quality and bias mitigation. Design robust evaluation metrics and benchmarks for LLM-based assistants in customer service domains. Work closely with engineering and product teams to integrate research into production environments. Collaborate with legal and compliance teams to ensure responsible use of data and models. Stay current with academic and industry advances in LLMs, ASR, TTS, RLHF, and multimodal learning. Requirements: PhD in Machine Learning, Natural Language Processing, Computer Science, or a related field. 5+ years of hands-on experience in deep learning. Proven track record of research innovation, including published work or deployed systems. Strong programming skills in Python and deep learning frameworks like PyTorch. Demonstrated expertise in at least one domain area such as reinforcement learning, conversational AI, audio modelling, or LLM alignment. Experience leading projects end-to-end, from ideation to deployment. Excellent communication skills with the ability to write clear technical documents and explain complex concepts to diverse audiences. Comfortable working in ambiguity and driving clarity through experimentation and data. Preferred Qualifications: Experience with speech technologies such as ASR and TTS. Familiarity with cloud environments (AWS, GCP, Azure). Exposure to RLHF, reward modelling, or human preference data collection. Prior work on real-time systems, streaming inference, or memory-efficient model deployment. Benefits Participation in the company's employee share options plan 25 days holiday, plus bank holidays Flexible working from home policy Work from outside of the UK for up to 6 months each year Enhanced parental leave Bike2Work scheme Annual learning and development allowance One-off WFH allowance when you join Company-funded fertility and family-forming programmes Menopause care programme with Maven Private healthcare and dental cover, discounts on gym members and relaxation apps, and access to a range of mental health programs At PolyAI, we take great pride in our values-they guide everything we do. We believe that a strong culture leads to meaningful work and lasting impact. Our core values are: Only the best: We expect the best from our people, we hire people that expect the best from themselves, and we nurture this drive for excellence. Ownership: We care deeply about what we do. We take ownership of our initiatives, decisions and outcomes. Relentlessly improve : We demand more from ourselves and are always evolving. Continuous, obsessive improvement is the only way we will transform the world of conversational AI. Bias for action: Our world moves quickly and so do we. We take calculated risks and we deliver impact fast. Disagree and commit: We are all working toward the same goal. If we dont agree with something, we work hard to understand it and when a decision is made, we accept it and give it our all. Build for people: We are hyper-focused on delivering the best automated experiences possible so that we can empower people to get exactly what they need, when they need it. PolyAI is proud to be an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All employment decisions at PolyAI will be based on the business needs without attention to ethnicity, religion, sexual orientation, gender identity, family or parental status, national origin, neurodiversity status or disability status. Kindly find the Privacy Notice for our recruitment process by following the link here . This document provides important information regarding how we handle your personal data throughout the recruitment journey. Apply for this job indicates a required field First Name Last Name Preferred First Name Email Phone Resume/CV Enter manually Accepted file types: pdf, doc, docx, txt, rtf Education School Select Degree Select Select Start date year End date year LinkedIn Profile What are your salary expectations for this role? What is your current notice period? Where did you go to university? What level of qualification did you achieve? What did you study? When did/do you graduate? Will you now or in the future require any visa or sponsorship support to work in the United Kingdom? Select
Jul 16, 2025
Full time
Senior Research Scientist (LLM post training) United Kingdom PolyAI automates customer service through lifelike voice assistants that let customers lead a conversation. Our voice assistants make it possible for businesses to deliver outstanding customer service that rivals their human agents. Our customers, which include the world's leading logos, are expanding how they use our platform, driving automation of critical customer service operations and integrating PolyAI into their daily customer service workflows. We are looking for a Senior Research Scientist to join our world-class team and lead cutting-edge work on large language model (LLM) post-training. This is not just about applying standard fine-tuning techniques - it's about building the future of dialogue systems with novel approaches to reasoning, reinforcement learning, audio-first LLMs, and more. As a Senior Research Scientist at PolyAI, you'll lead impactful research projects from ideation through to deployment. You'll be driving innovation in how we train and adapt LLMs for real-world conversations - spanning voice, text, and multimodal contexts. You'll work on frontier techniques such as: Conversational reinforcement learning Streaming and continuous turn-taking Audio-native LLMs Distillation of reasoning models Long-context You'll also play a key role in shaping the scientific direction of our research, mentoring junior colleagues, and collaborating cross-functionally to bring research into production. Responsibilities: Lead and execute complex research projects with clear business impact. Design and implement novel post-training strategies including preference tuning, reward modeling, and synthetic supervision. Develop innovative model architectures and training approaches for conversational AI, including speech-aware and multimodal models. Conduct empirical studies to assess model performance in live deployments and iterate quickly based on real-world data. Generate, collect, and annotate training data - including synthetic and real-world conversational datasets - with an eye for quality and bias mitigation. Design robust evaluation metrics and benchmarks for LLM-based assistants in customer service domains. Work closely with engineering and product teams to integrate research into production environments. Collaborate with legal and compliance teams to ensure responsible use of data and models. Stay current with academic and industry advances in LLMs, ASR, TTS, RLHF, and multimodal learning. Requirements: PhD in Machine Learning, Natural Language Processing, Computer Science, or a related field. 5+ years of hands-on experience in deep learning. Proven track record of research innovation, including published work or deployed systems. Strong programming skills in Python and deep learning frameworks like PyTorch. Demonstrated expertise in at least one domain area such as reinforcement learning, conversational AI, audio modelling, or LLM alignment. Experience leading projects end-to-end, from ideation to deployment. Excellent communication skills with the ability to write clear technical documents and explain complex concepts to diverse audiences. Comfortable working in ambiguity and driving clarity through experimentation and data. Preferred Qualifications: Experience with speech technologies such as ASR and TTS. Familiarity with cloud environments (AWS, GCP, Azure). Exposure to RLHF, reward modelling, or human preference data collection. Prior work on real-time systems, streaming inference, or memory-efficient model deployment. Benefits Participation in the company's employee share options plan 25 days holiday, plus bank holidays Flexible working from home policy Work from outside of the UK for up to 6 months each year Enhanced parental leave Bike2Work scheme Annual learning and development allowance One-off WFH allowance when you join Company-funded fertility and family-forming programmes Menopause care programme with Maven Private healthcare and dental cover, discounts on gym members and relaxation apps, and access to a range of mental health programs At PolyAI, we take great pride in our values-they guide everything we do. We believe that a strong culture leads to meaningful work and lasting impact. Our core values are: Only the best: We expect the best from our people, we hire people that expect the best from themselves, and we nurture this drive for excellence. Ownership: We care deeply about what we do. We take ownership of our initiatives, decisions and outcomes. Relentlessly improve : We demand more from ourselves and are always evolving. Continuous, obsessive improvement is the only way we will transform the world of conversational AI. Bias for action: Our world moves quickly and so do we. We take calculated risks and we deliver impact fast. Disagree and commit: We are all working toward the same goal. If we dont agree with something, we work hard to understand it and when a decision is made, we accept it and give it our all. Build for people: We are hyper-focused on delivering the best automated experiences possible so that we can empower people to get exactly what they need, when they need it. PolyAI is proud to be an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All employment decisions at PolyAI will be based on the business needs without attention to ethnicity, religion, sexual orientation, gender identity, family or parental status, national origin, neurodiversity status or disability status. Kindly find the Privacy Notice for our recruitment process by following the link here . This document provides important information regarding how we handle your personal data throughout the recruitment journey. Apply for this job indicates a required field First Name Last Name Preferred First Name Email Phone Resume/CV Enter manually Accepted file types: pdf, doc, docx, txt, rtf Education School Select Degree Select Select Start date year End date year LinkedIn Profile What are your salary expectations for this role? What is your current notice period? Where did you go to university? What level of qualification did you achieve? What did you study? When did/do you graduate? Will you now or in the future require any visa or sponsorship support to work in the United Kingdom? Select
About the Team The Post-Training Team is dedicated to optimising AI systems to achieve state-of-the-art performance across the various risk domains that AISI focuses on. This is accomplished through a combination of scaffolding, prompting, supervised and RL fine-tuning of the AI models which AISI has access to. One of the main focuses of our evaluation teams is estimating how new models might affect the capabilities of AI systems in specific domains. To improve confidence in our assessments, we make significant effort to enhance the model's performance in the domains of interest. For many of our evaluations, this means taking a model we have been given access to and embedding it as part of a wider AI system-for example, in our cybersecurity evaluations, we provide models with access to tools for interacting with the underlying operating system and repeatedly call models to act in such environment. In our evaluations which do not require agentic capabilities, we may use elicitation techniques like fine-tuning and prompt engineering to ensure assessing the model at its full capacity. About the Role As a member of this team, you will use cutting-edge machine learning techniques to improve model performance in our domains of interest. The work is split into two sub-teams: Agents and Finetuning. Our Agents sub-team focuses on developing the LLM tools and scaffolding to create highly capable LLM-based agents, while our Finetuning Team builds out finetuning pipelines to improve models on our domains of interest. The Post-Training Team is seeking strong Research Scientists to join the team. The priorities of the team include both research-oriented tasks-such as designing new techniques for scaling inference-time computation or developing methodologies for in-depth analysis of agent behaviour-and engineering-oriented tasks-like implementing new tools for our LLM agents or creating pipelines for supporting and fine-tuning large open-source models. We recognise that some technical staff may prefer to span or alternate between engineering and research responsibilities, and this versatility is something we actively look for in our hires. You'll receive mentorship and coaching from your manager and the technical leads on your team, and regularly interact with world-class researchers and other exceptional staff, including alumni from Anthropic, DeepMind and OpenAI. In addition to junior roles, we offer Senior, Staff, and Principal Research Engineer positions for candidates with the requisite seniority and experience. Person Specification You may be a good fit if you have some of the following skills, experience and attitudes: Experience conducting empirical machine learning research (e.g. PhD in a technical field and/or papers at top ML conferences), particularly on LLMs. Experience with machine learning engineering, or extensive experience as a software engineer with a strong demonstration of relevant skills/knowledge in the machine learning. An ability to work autonomously and in a self-directed way with high agency, thriving in a constantly changing environment and a steadily growing team, while figuring out the best and most efficient ways to solve a particular problem. Particularly strong candidates also have the following experience: Building LLM agents in industry or open-source collectives, particularly in areas adjacent to the main interests of one of our workstreams e.g. in-IDE coding assistants, research assistants etc. (for our Agents subteam) Leading research on improving and measuring the capabilities of LLM agents (for our Agents sub-team) Building pipelines for fine-tuning (or pretraining LLMs). Finetuning with RL techniques is particularly relevant (for our finetuning subteam). Finetuning or pretraining LLMs in a research context, particularly to achieve increased performance in specific domains (for our finetuning subteam). Salary & Benefits We are hiring individuals at all ranges of seniority and experience within the research unit, and this advert allows you to apply for any of the roles within this range. We will discuss and calibrate with you as part of the process. The full range of salaries available is as follows: L3: £65,000 - £75,000 L4: £85,000 - £95,000 L5: £105,000 - £115,000 L6: £125,000 - £135,000 L7: £145,000 There are a range of pension options available which can be found through the Civil Service website. Selection Process In accordance with the Civil Service Commission rules, the following list contains all selection criteria for the interview process. Required Experience We select based on skills and experience regarding the following areas: Research problem selection Research Engineering Writing code efficiently Python Frontier model architecture knowledge Frontier model training knowledge Model evaluations knowledge AI safety research knowledge Written communication Verbal communication Teamwork Interpersonal skills Tackle challenging problems Learn through coaching Desired Experience We additionally may factor in experience with any of the areas that our work-streams specialise in: Cyber security Chemistry or Biology Safeguards Safety Cases Societal Impacts
Feb 11, 2025
Full time
About the Team The Post-Training Team is dedicated to optimising AI systems to achieve state-of-the-art performance across the various risk domains that AISI focuses on. This is accomplished through a combination of scaffolding, prompting, supervised and RL fine-tuning of the AI models which AISI has access to. One of the main focuses of our evaluation teams is estimating how new models might affect the capabilities of AI systems in specific domains. To improve confidence in our assessments, we make significant effort to enhance the model's performance in the domains of interest. For many of our evaluations, this means taking a model we have been given access to and embedding it as part of a wider AI system-for example, in our cybersecurity evaluations, we provide models with access to tools for interacting with the underlying operating system and repeatedly call models to act in such environment. In our evaluations which do not require agentic capabilities, we may use elicitation techniques like fine-tuning and prompt engineering to ensure assessing the model at its full capacity. About the Role As a member of this team, you will use cutting-edge machine learning techniques to improve model performance in our domains of interest. The work is split into two sub-teams: Agents and Finetuning. Our Agents sub-team focuses on developing the LLM tools and scaffolding to create highly capable LLM-based agents, while our Finetuning Team builds out finetuning pipelines to improve models on our domains of interest. The Post-Training Team is seeking strong Research Scientists to join the team. The priorities of the team include both research-oriented tasks-such as designing new techniques for scaling inference-time computation or developing methodologies for in-depth analysis of agent behaviour-and engineering-oriented tasks-like implementing new tools for our LLM agents or creating pipelines for supporting and fine-tuning large open-source models. We recognise that some technical staff may prefer to span or alternate between engineering and research responsibilities, and this versatility is something we actively look for in our hires. You'll receive mentorship and coaching from your manager and the technical leads on your team, and regularly interact with world-class researchers and other exceptional staff, including alumni from Anthropic, DeepMind and OpenAI. In addition to junior roles, we offer Senior, Staff, and Principal Research Engineer positions for candidates with the requisite seniority and experience. Person Specification You may be a good fit if you have some of the following skills, experience and attitudes: Experience conducting empirical machine learning research (e.g. PhD in a technical field and/or papers at top ML conferences), particularly on LLMs. Experience with machine learning engineering, or extensive experience as a software engineer with a strong demonstration of relevant skills/knowledge in the machine learning. An ability to work autonomously and in a self-directed way with high agency, thriving in a constantly changing environment and a steadily growing team, while figuring out the best and most efficient ways to solve a particular problem. Particularly strong candidates also have the following experience: Building LLM agents in industry or open-source collectives, particularly in areas adjacent to the main interests of one of our workstreams e.g. in-IDE coding assistants, research assistants etc. (for our Agents subteam) Leading research on improving and measuring the capabilities of LLM agents (for our Agents sub-team) Building pipelines for fine-tuning (or pretraining LLMs). Finetuning with RL techniques is particularly relevant (for our finetuning subteam). Finetuning or pretraining LLMs in a research context, particularly to achieve increased performance in specific domains (for our finetuning subteam). Salary & Benefits We are hiring individuals at all ranges of seniority and experience within the research unit, and this advert allows you to apply for any of the roles within this range. We will discuss and calibrate with you as part of the process. The full range of salaries available is as follows: L3: £65,000 - £75,000 L4: £85,000 - £95,000 L5: £105,000 - £115,000 L6: £125,000 - £135,000 L7: £145,000 There are a range of pension options available which can be found through the Civil Service website. Selection Process In accordance with the Civil Service Commission rules, the following list contains all selection criteria for the interview process. Required Experience We select based on skills and experience regarding the following areas: Research problem selection Research Engineering Writing code efficiently Python Frontier model architecture knowledge Frontier model training knowledge Model evaluations knowledge AI safety research knowledge Written communication Verbal communication Teamwork Interpersonal skills Tackle challenging problems Learn through coaching Desired Experience We additionally may factor in experience with any of the areas that our work-streams specialise in: Cyber security Chemistry or Biology Safeguards Safety Cases Societal Impacts
DESCRIPTION Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting-edge Generative AI algorithms to solve real-world problems with significant impact? The AWS Industries Team helps AWS customers implement Generative AI solutions and realize transformational business opportunities for AWS customers in the most strategic industry verticals. This is a team of data scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select, train, and fine-tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and build applications to launch these solutions at scale. The AWS Industries team provides guidance and implements best practices for applying generative AI responsibly and cost-efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. In this Data Scientist role, you will be capable of using GenAI and other techniques to design, evangelize, implement, and scale cutting-edge solutions for never-before-solved problems. Key job responsibilities As a Senior Data Scientist, you will: Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms and build ML systems to address real-world challenges. Interact with customers directly to understand the business problem, aid them in the implementation of generative AI solutions, deliver briefing and deep dive sessions to customers, and guide customers on adoption patterns and paths to production. Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholders. Provide customer and market feedback to Product and Engineering teams to help define product direction. BASIC QUALIFICATIONS Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or Master's degree. Experience working as a Data Scientist. Experience with data scripting languages (e.g., SQL, Python, R, or equivalent) or statistical/mathematical software (e.g., R, SAS, Matlab, or equivalent). Experience in machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance. Experience applying theoretical models in an applied environment. PREFERRED QUALIFICATIONS PhD in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science. Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance. Hands-on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer). Prior experience in training and fine-tuning of Large Language Models (LLMs) and knowledge of AWS platform and tools or equivalent cloud experience. Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build.
Feb 02, 2025
Full time
DESCRIPTION Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting-edge Generative AI algorithms to solve real-world problems with significant impact? The AWS Industries Team helps AWS customers implement Generative AI solutions and realize transformational business opportunities for AWS customers in the most strategic industry verticals. This is a team of data scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select, train, and fine-tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and build applications to launch these solutions at scale. The AWS Industries team provides guidance and implements best practices for applying generative AI responsibly and cost-efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. In this Data Scientist role, you will be capable of using GenAI and other techniques to design, evangelize, implement, and scale cutting-edge solutions for never-before-solved problems. Key job responsibilities As a Senior Data Scientist, you will: Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms and build ML systems to address real-world challenges. Interact with customers directly to understand the business problem, aid them in the implementation of generative AI solutions, deliver briefing and deep dive sessions to customers, and guide customers on adoption patterns and paths to production. Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholders. Provide customer and market feedback to Product and Engineering teams to help define product direction. BASIC QUALIFICATIONS Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or Master's degree. Experience working as a Data Scientist. Experience with data scripting languages (e.g., SQL, Python, R, or equivalent) or statistical/mathematical software (e.g., R, SAS, Matlab, or equivalent). Experience in machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance. Experience applying theoretical models in an applied environment. PREFERRED QUALIFICATIONS PhD in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science. Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance. Hands-on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer). Prior experience in training and fine-tuning of Large Language Models (LLMs) and knowledge of AWS platform and tools or equivalent cloud experience. Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build.
Job ID: AWS EMEA SARL (UK Branch) Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The AWS Industries Team helps AWS customers implement Generative AI solutions and realize transformational business opportunities for AWS customers in the most strategic industry verticals. This is a team of data scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select, train, and fine-tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and build applications to launch these solutions at scale. The AWS Industries team provides guidance and implements best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. Key job responsibilities As a Senior Data Scientist, you will: Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms and build ML systems to address real-world challenges. Interact with customers directly to understand the business problem, aid them in the implementation of generative AI solutions, deliver briefing and deep dive sessions to customers, and guide customers on adoption patterns and paths to production. Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholders. Provide customer and market feedback to Product and Engineering teams to help define product direction. About the team Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud. Inclusive Team Culture Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship, and other career-advancing resources here to help you develop into a better-rounded professional. BASIC QUALIFICATIONS Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or Master's degree. Experience working as a Data Scientist. Experience with data scripting languages (e.g., SQL, Python, R, or equivalent) or statistical/mathematical software (e.g., R, SAS, Matlab, or equivalent). Experience in machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience. Experience applying theoretical models in an applied environment. PREFERRED QUALIFICATIONS PhD in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science. Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience. Hands-on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer). Prior experience in training and fine-tuning of Large Language Models (LLMs) and knowledge of AWS platform and tools or equivalent cloud experience. Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice () to know more about how we collect, use and transfer the personal data of our candidates. Posted: October 16, 2024 (Updated 1 day ago)
Feb 02, 2025
Full time
Job ID: AWS EMEA SARL (UK Branch) Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The AWS Industries Team helps AWS customers implement Generative AI solutions and realize transformational business opportunities for AWS customers in the most strategic industry verticals. This is a team of data scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select, train, and fine-tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and build applications to launch these solutions at scale. The AWS Industries team provides guidance and implements best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. Key job responsibilities As a Senior Data Scientist, you will: Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms and build ML systems to address real-world challenges. Interact with customers directly to understand the business problem, aid them in the implementation of generative AI solutions, deliver briefing and deep dive sessions to customers, and guide customers on adoption patterns and paths to production. Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholders. Provide customer and market feedback to Product and Engineering teams to help define product direction. About the team Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud. Inclusive Team Culture Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship, and other career-advancing resources here to help you develop into a better-rounded professional. BASIC QUALIFICATIONS Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or Master's degree. Experience working as a Data Scientist. Experience with data scripting languages (e.g., SQL, Python, R, or equivalent) or statistical/mathematical software (e.g., R, SAS, Matlab, or equivalent). Experience in machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience. Experience applying theoretical models in an applied environment. PREFERRED QUALIFICATIONS PhD in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science. Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience. Hands-on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer). Prior experience in training and fine-tuning of Large Language Models (LLMs) and knowledge of AWS platform and tools or equivalent cloud experience. Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice () to know more about how we collect, use and transfer the personal data of our candidates. Posted: October 16, 2024 (Updated 1 day ago)
