LLM Performance Engineer — Scale, Optimize & Deploy

Isomorphic Labs; Greater London; Hybrid; GBP 120,000 - 160,000; Full time

Job summary

Isomorphic Labs in the United Kingdom is seeking an experienced ML engineer to scale frontier models in production. You will join the model performance and scaling team, partnering with scientists and engineers to accelerate model training and deployment for drug discovery.

You will apply novel techniques to performance optimization, leverage large-scale distributed training, and translate research advances into robust, production‑ready systems that impact real-world biopharma projects.

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
• Excellent collaboration skills.

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 training of LLMs

DL frameworks (JAX or PyTorch)

Parallelism & NCCL

GPU architectures

Collaboration

Job description

Isomorphic Labs in the United Kingdom is seeking an experienced ML engineer to scale frontier models in production. You will join the model performance and scaling team, partnering with scientists and engineers to accelerate model training and deployment for drug discovery.

You will apply novel techniques to performance optimization, leverage large-scale distributed training, and translate research advances into robust, production‑ready systems that impact real-world biopharma projects.