Machine Learning Research Engineer

The opportunity

Relation is offering an outstanding opportunity for a Machine Learning Research Engineer to help build and scale the next generation of generative and predictive models of cellular behaviour. Research Engineers at Relation are software engineers with a deep understanding of machine learning and deep learning, acting as a critical bridge between theory and implementation: designing, building, and scaling the complex systems on which our ML research depends. You'll be joining a team with access to cutting-edge multiomic and interventional datasets, advanced computational infrastructure, and deep interdisciplinary expertise, and a culture that embraces modern ML tooling, including agentic workflows. Key responsibilities include implementing and optimising large neural network models; building robust infrastructure for distributed training, evaluation, and inference; and partnering with ML Scientists to take research from prototype to production-grade systems applied to large, multi-modal biological data, tested directly in experimental biology.

Day to day, you will

• Implement and optimise large models, partnering with ML Scientists to translate research ideas into reproducible, scalable training pipelines.

• Profile and optimise training across compute, memory, and I/O, pursuing measurable gains in throughput, convergence, and stability.

• Design and implement distributed training strategies across multi-GPU and multi-node configurations.

• Build and maintain core ML infrastructure.

• Contribute to architectural and algorithmic decisions, bringing engineering judgment into research discussions.

• Optimise inference and downstream deployment so models can be used by data scientists and biologists in our discovery workflows.

• Address numerical, performance, and reliability issues across the stack.

• Establish and maintain engineering practices in research code.

• Track developments in ML systems and bring relevant advances into our stack.

Professionally, you will have

• A degree in Computer Science, Engineering, Physics, or a related quantitative discipline; industry experience as an ML / research engineer working on large neural network training.

• Strong software engineering fundamentals in Python and deep expertise in PyTorch (or equivalent modern ML frameworks).

• Hands-on experience training large neural networks at scale, including distributed training frameworks.

• Demonstrable experience profiling and optimising GPU workloads.

• Working knowledge of cloud-based ML infrastructure and containerised environments.

• A track record of taking research code from prototype to robust, reusable infrastructure that other people actually use.

  Bonus experience:

• CUDA / Triton kernel development; FlashAttention-style attention implementations; experience with foundation models for biology, vision, or language; contributions to open-source ML frameworks.