Machine Learning Research Engineer

The role

You will design, build, and scale complex systems for machine learning research. You will implement and optimise large neural network models and build infrastructure for distributed training, evaluation, and inference.

What you will do

• Implement and optimise large models
• Translate research ideas into reproducible, scalable training pipelines
• Profile and optimise training across compute, memory, and I/O
• 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
• Optimise inference and downstream deployment
• Address numerical, performance, and reliability issues across the stack
• Establish and maintain engineering practices in research code
• Track developments in ML systems

Required qualifications

• 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
• 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
• Track record of taking research code from prototype to robust, reusable infrastructure

Nice to have

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

About the company

Relation is a TechBio company developing transformational medicines. They leverage single-cell multi-omics, functional assays, and machine learning to drive disease understanding.