A fast-growing TechBio company that develops new medicines by combining single-cell multi-omics from patient tissue, functional assays, and machine learning. The team is highly interdisciplinary, with biology, computation, and engineering working side by side in state-of-the-art wet and dry labs.
The opportunity
Help build and scale the next generation of generative and predictive models of cellular behaviour. Research Engineers here are software engineers with deep ML expertise who bridge theory and implementation: they design, build, and scale the systems the ML research depends on. You’ll implement and optimise large neural networks, build infrastructure for distributed training, evaluation, and inference, and partner with ML Scientists to turn research prototypes into production-grade systems for large, multi-modal biological data.
What you’ll do
- Implement and optimise large models, turning research ideas into reproducible, scalable training pipelines.
- Profile and optimise training across compute, memory, and I/O to improve throughput, convergence, and stability.
- Design and implement distributed training strategies across multi-GPU and multi-node setups.
- Build and maintain core ML infrastructure.
- Contribute engineering judgment to architectural and algorithmic decisions.
- Optimise inference and deployment so data scientists and biologists can use models in discovery workflows.
- Resolve numerical, performance, and reliability issues across the stack.
- Establish strong engineering practices in research code.
- Track advances in ML systems and bring relevant ones into the stack.
What you’ll bring
- A degree in Computer Science, Engineering, Physics, or a related quantitative discipline.
- Industry experience as an ML or research engineer working on large neural network training.
- Strong Python fundamentals and deep expertise in PyTorch or an equivalent framework.
- Hands‑on experience training large neural networks at scale, including with distributed training frameworks.
- Proven experience profiling and optimising GPU workloads.
- Working knowledge of cloud ML infrastructure and containerised environments.
- A track record of turning research code into robust, reusable infrastructure that others rely on.
Nice to have
- FlashAttention-style attention implementations.
- Experience with foundation models for biology, vision, or language.
- Contributions to open-source ML frameworks.
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