Machine Learning Researcher

Jobtailor; Cambridge; On-site; GBP 55,000 - 90,000; Full time

Job summary

Jobtailor is seeking a highly skilled ML researcher in Cambridge to lead an independent research program focused on probabilistic models, Bayesian optimization, and related fields. You will collaborate with research teams to meet ambitious goals and report findings internally and externally.

You will apply cutting-edge ML to real-world problems and contribute to open source libraries such as Trieste, GPflow, and GPflux.

Qualifications

• PhD in a technical field or equivalent experience.
• Published work in ML, statistics, or optimization conferences/journals.
• Background in decision making including Bayesian optimization, bandits, RL, 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 applying ML to real-world problems (advantage).
• Willingness to work as part of a team, review documents and code, and provide constructive feedback.
• Fresh PhD graduates to experienced team leads are welcome.

Responsibilities

• Develop an independent research program focused on 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.

Skills

PhD in technical field

Published ML work

Bayesian optimization

Python numerical

TensorFlow/PyTorch

Open source contribution

Education

PhD in technical field

Tools

NumPy

TensorFlow

PyTorch

Trieste

GPflow

GPflux

Job description

• 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

Requirements

• 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

Core Competencies

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.

Highest-signal resume keywords

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

ATS Optimization Keywords

Hard Skills

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

Soft Skills

• Team Collaboration
• Constructive Feedback
• Passion For Learning

Industry Keywords

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

Tools & Technologies

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