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Isomorphic Labs is applying frontier AI to accelerate drug discovery and development. The company was launched in 2021 to advance human health by building on the Nobel-winning AlphaFold system. The team has developed powerful predictive and generative AI models that accelerate scientific discovery, with a world-leading drug design engine capable of working across multiple therapeutic areas and drug modalities.
You will join the model performance and scaling team as a Research Engineer focused on LLM performance optimization. Working in a highly creative, iterative environment, you will partner with scientists and engineers to scale foundational models that will transform the biopharmaceutical world.
Key responsibilities include:
- 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
You will draw upon existing engineering experience while learning from colleagues to apply novel techniques to model and systems performance optimizations, as well as machine learning, computational biology, and chemistry problems.
The company operates on a hybrid model requiring 3 days per week in the London office (currently Tuesday, Wednesday, and one other day depending on team).
REQUIREMENTS:
Essential:
- Significant experience with large-scale distributed training of LLMs
- Experience with deep learning ML frameworks (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
Nice to have:
- Experience with general LLM serving stacks
- Knowledge of XLA, Triton, Pallas, CUDA, or similar accelerator DSLs/compilers
- Experience optimizing ML accuracy using low-precision formats
- Prior experience building, deploying, and maintaining production systems on GCP
- Interest in chemistry and biology
