Distributed Training Engineer

Department: Bits: Research, LLMs, machine learning, infra

Location: Menlo Park, Remote

Employment Type: FullTime

About Periodic Labs

We are an AI + physical sciences lab building state of the art models to make novel scientific discoveries. We are well funded and growing rapidly. Team members are owners who identity and solve problems without boundaries or bureaucracy. We eagerly learn new tools and new science to push forward our mission.

About the role

You will optimize, operate and develop large-scale distributed LLM training systems that power AI scientific research. You will work closely with researchers to bring up, debug, and maintain mid-training and reinforcement learning workflows. You will build tools and directly support frontier-scale experiments to make Periodic Labs the world’s best AI + science lab for physicists, computational materials scientists, AI researchers, and engineers. You will contribute open-source large scale LLM training frameworks.

You might thrive in this role if you have experience with:

• Training on clusters with ≥5,000 GPUs

• 5D parallel LLM training

• Distributed training frameworks such as Megatron-LM, FSDP, DeepSpeed, TorchTitan

• Optimizing training throughput for large scale Mixture-of-Expert models