Machine Learning Researcher

Hlx Life Sciences; Greater London; On-site; GBP 70,000 - 120,000; Full time

Job summary

HLX Life Sciences in London is seeking a Research Engineer to design, implement, and scale generative and predictive models of cellular behaviour. You will bridge ML research and production, building infrastructure for distributed training, evaluation, and inference across multi-modal biological data.

You will optimize training pipelines, profile GPU workloads, and collaborate with ML scientists to turn prototypes into robust, reusable systems.

Qualifications

• Degree in Computer Science, Engineering, Physics, or related quantitative discipline.
• Industry experience as ML or research engineer training large neural networks.
• Strong Python and PyTorch fundamentals.
• Hands-on experience with training large neural networks at scale.
• Experience profiling and optimising GPU workloads.
• Knowledge of cloud ML infrastructure and containerised environments.
• Proven ability turning research code into robust, reusable infra.

Responsibilities

• Implement and optimise large models for scalable training pipelines.
• Profile and optimise training across compute, memory, and I/O.
• Design distributed training strategies across multi-GPU and multi-node setups.
• Build and maintain core ML infrastructure.
• Contribute engineering judgment to architectural decisions.
• Optimise inference and deployment for discovery workflows.
• Address numerical, performance, and reliability issues.
• Establish strong engineering practices in research code.
• Track ML system advances and integrate relevant ones.

Skills

Python fundamentals

Distributed training

ML systems design

GPU profiling

Collaboration with ML scientists

Education

Bachelors in Computer Science/Engineering/Physics or related

Tools

PyTorch

Distributed training frameworks

Cloud ML infrastructure

Containerised environments

Job description

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.