ML Research Engineer, London

Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed.

Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases.

We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design.

Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI.

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.

• 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.

• 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)

• 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

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

• 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.

• Industry Experience: 4+ years of experience working in reputable tech companies or research labs.
• 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.

• 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.
• 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)

We are guided by our shared values. It's not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it. 

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