Research Engineer: Frontier AI for Drug Design (London)

Isomorphic Labs; London; Hybrid; NOK 877,000 - 1,504,000; Full time

Job summary

Isomorphic Labs is advancing frontier AI to accelerate drug discovery and design. You will join the model performance and scaling team, collaborating with scientists and engineers to scale foundational models that could transform biopharma.

Your prior engineering experience will be applied to novel techniques for performance optimizations across ML, computational biology and chemistry challenges. You will contribute to a culture of collaboration and continuous learning, while embracing a hybrid

Qualifications

• 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; focus on performance concepts over kernel writing.
• 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 experience

GPU architecture understanding

Parallelism strategies

Collaboration skills

Performance optimization

Tools

JAX

PyTorch

NCCL

XLA

Triton

CUDA

Job description

Isomorphic Labs is advancing frontier AI to accelerate drug discovery and design. You will join the model performance and scaling team, collaborating with scientists and engineers to scale foundational models that could transform biopharma.

Your prior engineering experience will be applied to novel techniques for performance optimizations across ML, computational biology and chemistry challenges. You will contribute to a culture of collaboration and continuous learning, while embracing a hybrid