Machine Learning Researcher

• Develop an independent research program focused on new tools and techniques for probabilistic models, Bayesian optimization, and related fields
• Collaborate with research teams to meet research goals
• Report research findings internally and externally
• Contribute to internal product development and customer research projects
• Apply cutting-edge machine learning research to real-world problems
• Participate in developing open source libraries such as Trieste, GPflow, and GPflux
• Develop theoretically rigorous and scalable algorithms
• Publish papers at leading machine learning conferences

• A PhD in a technical field or an equivalent level of experience
• Published work in machine learning, statistics, or optimization conferences and/or journals
• Background in decision making, including Bayesian optimization, bandits, reinforcement learning, or active learning (advantage)
• Background in probabilistic modelling and methods, including Gaussian processes, Bayesian neural networks, or variational inference (advantage)
• Experience in numerical programming with Python, NumPy, TensorFlow, or PyTorch (advantage)
• Experience or interest in applying machine learning to solve real-world problems (advantage)
• Willingness to work as part of a team, review documents and code, and provide constructive feedback
• Passion for continuously developing machine learning and research skills and helping others improve theirs
• Applicants may range from fresh PhD graduates to experienced team leads

Demonstrates expertise in developing probabilistic models and Bayesian optimization techniques, with a strong background in machine learning applications and research publication. Capable of collaborating effectively within research teams and contributing to open source projects while applying advanced algorithms to real-world challenges.

• PhD In A Technical Field
• Published Work In Machine Learning
• Bayesian Optimization
• Numerical Programming With Python
• Experience With TensorFlow Or PyTorch

• Probabilistic Modelling
• Bayesian Neural Networks
• Gaussian Processes
• Variational Inference
• Reinforcement Learning
• Active Learning
• Scalable Algorithms
• Machine Learning Research
• Statistical Analysis
• Algorithm Development

• Team Collaboration
• Constructive Feedback
• Passion For Learning

• Machine Learning
• Optimization
• Research Publication
• Decision Making
• Open Source Development

• NumPy
• TensorFlow
• PyTorch
• Trieste
• GPflow
• GPflux