Research Scientist, Machine Learning

Cambridge / Hybrid (Flexible 1 day per week in the office)

You MUST have a PhD to apply

Open to recent graduates, through to experienced research Leaders (Salary will match your level)

We build data-efficient engineering AI software that helps teams make better design and performance optimisation decisions, faster. Our technology reduces simulation and testing requirements without compromising quality — accelerating development and enabling more sustainable engineering.

Founded in 2016, the company is a fast-growing, well-funded AI scale-up that has received significant industry recognition.

We’re looking for a Research Scientist to join a research-focused team working at the cutting edge of artificial intelligence and machine learning.

You’ll be part of highly collaborative research groups developing algorithms that are both theoretically rigorous and scalable to real-world complexity. The team’s work directly impacts customer challenges and contributes to the wider ML community — with 75+ papers published and 10 patents filed in the last four years.

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• Develop and lead your own research programme in probabilistic modelling, Bayesian optimisation, and related fields
  
• Collaborate with colleagues to meet research goals and communicate findings internally and externally
  
• Contribute to product development and customer research projects, applying cutting-edge ML to real-world problems
  
• Take an active role in developing and maintaining open-source libraries such as Trieste, GPflow, and GPflux
  

We welcome applications from recent PhD graduates through to experienced research leaders. Above all, we’re looking for collaborative researchers who enjoy solving complex problems together.

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• PhD in a technical field or equivalent experience
  
• Published research in machine learning, statistics, or optimisation (conferences and/or journals)
  
• Experience in decision-making methods (Bayesian optimisation, bandits, reinforcement learning, active learning)
  
• Background in probabilistic modelling (Gaussian processes, Bayesian neural networks, variational inference, etc.)
  
• Strong numerical programming skills (Python, NumPy, TensorFlow, PyTorch)
  
• Interest in applying machine learning to real-world industrial challenges
  
• A collaborative mindset, including reviewing code and research and providing constructive feedback
  
• A passion for continuous learning and helping others grow
  

Google Scholar - Please share your link with your CV/ application

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• Competitive salary (reviewed annually)
  
• Pension salary sacrifice scheme
  
• Stock options (where applicable)
  
• Free 24/7 on-site gym
  
• Social events, game nights and sports groups
  

Apply Now or email me directly to learn more.