ML Systems Engineer for Scalable Biotech AI

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

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.