Senior AI Research Engineer

London | Visa Sponsorship & Relocation Support Available

Compensation: globally competitive

Days on site: 3 days ideally but open to flexibility

We’re partnered exclusively with one of Europe’s most ambitious AI research organisations working at the intersection of large-scale foundation models and biological intelligence.

The team is building frontier-scale generative models trained on one of the world’s largest proprietary biological datasets, with data scale expected to reach world leading token territory over the next 18 months.

This is not a traditional biotech company and it is not a standard ML engineering role.

The environment is a frontier AI lab. The organisation is running massive-scale training workloads across novel biological data modalities, solving optimisation and infrastructure problems at the edge of modern compute systems.

The work sits directly alongside research, where the systems you build influence what experiments are possible and how quickly the team can move.

The organisation has already attracted backing and partnerships from some of the most important names in frontier AI and compute infrastructure globally.

The Role

You’ll join a small, highly technical AI research group building the systems and infrastructure behind next-generation biological foundation models.

This role spans distributed training, accelerator optimisation, experiment orchestration, large-scale data infrastructure and research tooling. You’ll work closely with researchers across machine learning and computational biology to improve training performance, increase experimentation velocity and help scale increasingly ambitious model development efforts.

This is a role for someone who enjoys operating close to the limits of modern AI systems and wants to work on genuinely difficult engineering problems with direct research impact.

Why This Role Is Interesting

Most AI organisations are competing on increasingly similar public datasets.

This team’s advantage is fundamentally different. They are building proprietary biological datasets at enormous scale and using them to train generative systems capable of designing entirely new biological functions.

For engineers who want to work on frontier-scale training systems with real scientific impact, this is one of the rare opportunities currently available in Europe.

What They’re Looking For

Strong candidates will typically come from environments where large-scale model training and systems optimisation are core to the work. That could include frontier AI labs, foundation model teams, large-scale multimodal systems groups or high-performance distributed ML environments.

Experience in biology is not required. The team is far more interested in engineers who understand how to make ambitious research systems work reliably at scale and who are excited by applying those skills to one of the most interesting data domains in AI.

Areas of interest include:

• PyTorch/JAX internals
• CUDA, XLA or Triton optimisation
• Distributed training systems
• GPU cluster performance tuning
• Large-scale experiment management
• Training reliability and reproducibility
• Kubernetes and orchestration tooling
• Infrastructure supporting frontier AI research

Candidates with backgrounds in mathematics, physics, systems engineering or large-scale AI infrastructure are particularly relevant.