Research Engineer (LLM Performance), London

Overview

In this role you will advance frontier AI applied to drug design, helping to scale foundational models and optimize performance for real-world biomedical programs. You will work within an interdisciplinary team to translate research into production-ready systems, driving efficiency across distributed training and inference. The work combines machine learning, computational biology, and chemistry to accelerate discoveries. This opportunity offers impact at the intersection of AI and life sciences, with a culture focused on collaboration and curiosity.

Pay / Benefits

• hybrid working
• inclusive culture
• accommodation for disabilities

Responsibilities

• Implement and optimize LLM post-training methods at scale on frontier models
• Collaborate with research teams to translate methods into production-ready systems
• Prioritize and execute performance optimization opportunities
• Evaluate and deploy frameworks for supervised fine-tuning, RL, and LLM evaluation
• Diagnose and fix bottlenecks in distributed training and inference
• Deploy low-precision methods to balance performance with accuracy

Key requirements

• Significant experience with large scale distributed training of LLMs
• Experience with deep learning frameworks (JAX or PyTorch)
• Knowledge of parallelism strategies and NCCL
• Good understanding of GPU architectures
• Reasoning about performance concepts
• Excellent collaboration skills

• collaboration
• adaptability
• curiosity
• large scale distributed training of LLMs
• deep learning frameworks (JAX or PyTorch)
• parallelism strategies

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