LLM Performance Engineer — Scale Frontiers in AI

Isomorphic Labs; Greater London; Hybrid; GBP 80,000 - 120,000; Full time

Job summary

Isomorphic Labs in London is seeking a Research Engineer (LLM Performance) to join the model performance and scaling team. You will collaborate with scientists and engineers to scale foundational models that transform the biopharmaceutical world, applying novel techniques to optimize distributed training and inference.

You will draw on your engineering experience and learn from peers to deploy methods for supervised fine-tuning, RL and LLM evaluation, with a focus on performance and efficiency

Qualifications

• Significant experience with large scale distributed training of LLMs.
• Experience with deep learning ML frameworks (either JAX or PyTorch).
• Knowledge of parallelism strategies and collective communication libraries (e.g. NCCL).
• Good understanding of GPU architectures. Reasoning about performance concepts is more important than writing kernels from scratch

Responsibilities

• Implement and optimize LLM post-training methods at scale on frontier models.
• Collaborate with research teams to translate new methods into production-ready systems.
• Relentlessly prioritize and execute on performance optimization opportunities.
• Evaluate and deploy frameworks for supervised fine-tuning, reinforcement learning and LLM evaluation.
• Diagnose and fix performance bottlenecks and communication overhead in distributed training and inference systems.
• Deploy low-precision methods to balance performance with accuracy, impacting real world drug design programs.

Skills

Distributed LLM training

JAX/PyTorch

Parallelism strategies

GPU architectures

Tools

NCCL

CUDA

XLA

Triton

Job description

Isomorphic Labs in London is seeking a Research Engineer (LLM Performance) to join the model performance and scaling team. You will collaborate with scientists and engineers to scale foundational models that transform the biopharmaceutical world, applying novel techniques to optimize distributed training and inference.

You will draw on your engineering experience and learn from peers to deploy methods for supervised fine-tuning, RL and LLM evaluation, with a focus on performance and efficiency