ML Research Engineer, London

Research Engineering (Machine Learning), London 

We are looking for Research Engineers with different levels of experience - Mid through to Senior, Staff, Principal or equivalent levels.

Your impact 

This is an exciting opportunity for you to contribute to frontier research at the intersection of AI and drug design.

Working in a highly creative, iterative environment, you will be partnering with scientists and engineers to advance foundational models that will transform the biopharmaceutical world as we know it.  

You will draw upon your existing engineering and Machine Learning experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology and chemistry problems.

What you will do 

Implementation & Optimisation:

• Translate research concepts into practical implementations by developing and optimising state-of-the-art AI models, and building and maintaining robust codebases, data pipelines, and infrastructure for training and evaluation.

Experimentation & Evaluation:

• Design, implement, and run experiments to evaluate the performance and robustness of ML models, using a full spectrum of state-of-the-art machine learning methods. Evaluating, tuning, and maintaining AI/ML models (which includes collecting and preparing data as needed)

Evaluation & Inference:

• Implement algorithms and software to analyse and evaluate the performance of AI models.
• Optimising performance of AI/ML models such as Diffusion models, Transformers, GNNs, leveraging a deep understanding of the AI/ML hardware+software stack
• Advise on how to bring AI/ML models to production and/or integrating them into product offerings, and monitoring and refining their behavior.
• Developing specialised tools/frameworks/infrastructure to aid in the work above

Collaboration & Knowledge Sharing:

• Work closely with research scientists and engineers, contributing to team discussions, sharing knowledge, and actively participating in code reviews to foster a collaborative environment.

Innovation & Impact:

• Proactively identify and address technical challenges, stay updated on the latest AI advancements, and focus on developing solutions that enable scaling our wider foundation and applied model platforms.
• Ability to execute on independent engineering projects and software development towards research goals.

Skills and Qualifications

Essential

• Academic Background: Advanced degree (Master’s or PhD) in a highly quantitative field (Computer Science, AI, Physics, Mathematics, etc.) or equivalent practical experience.
• ML Fundamentals: Deep understanding of machine learning principles and techniques.
• Framework Expertise: Strong proficiency in deep learning frameworks such as JAX or PyTorch.
• Modern Architectures: Hands-on experience building and working with modern model architectures (e.g., Transformers, GNNs, Diffusion Models).
• Full ML Lifecycle: Experience taking models from conception to production (scoping, data analysis, training, debugging, evaluation, benchmarking, and deployment).
• Engineering Excellence: Excellent software development skills with strong algorithms and data structures fundamentals.
• Collaboration & Communication: An excellent team player with strong written and verbal communication skills, able to collaborate seamlessly in a cross-disciplinary environment.
• Agency: Self-directed with an ability to navigate ambiguity, propose and own complex projects, learn the necessary context, and readily adapt to new domains and developments.

Nice to have

• Proven Research Record: A history of scientific contributions (e.g., publications at NeurIPS, ICML, ICLR, CVPR) or significant contributions to state-of-the-art AI models.
• Scale & Performance: Experience training models across distributed systems (multi-GPU/multi-node) and optimising training and inference performance (e.g., XLA, Triton, CUDA, Pallas).
• Domain Knowledge: A strong interest in, or knowledge of, biochemistry, computational biology, or drug discovery fundamentals.
• Industry Experience: Proven track record working in reputable tech companies or research labs.
• Applied ML: Experience developing models developed for real-world applications.
• Infrastructure: Solid technical infrastructure knowledge and experience with low-level engineering (e.g., GCP, Kubernetes, Docker)