Research Engineer (Materials Foundation Models)

What you'll do

• Design and run scaling studies, ablations, and evaluations; train multimodal models at scale.
• Own model data curation and evaluation, and build scalable, reliable, maintainable systems.
• Collaborate with chemists and materials scientists to integrate computational and experimental workflows.

What they ask for

• Strong software engineering skills and a track record building complex, high-performance systems at scale in production or industry.
• Hands-on experience training or evaluating large models in distributed settings; senior candidates must have led a multi-hundred-GPU effort, while earlier-career candidates must have been a core contributor.
• Depth in at least one of large-scale vision or representation learning, LLM pre-training or post-training, or reinforcement learning.
• Master’s or PhD in CS, ML, or a related quantitative field, or equivalent experience; representation-learning and computer-vision experience is also specified.

Nice to have

• RL for post-training or agentic settings; frontier-lab or large-scale model-shipping experience.
• Machine-learning experience in materials science, chemistry, or molecular informatics; exposure to multimodal training.

Tools

SFT, RL, RLHF, GRPO

AI focus

Train and evaluate foundation models for materials discovery, including crystal-structure and property representation learning, scientific LLM fine-tuning and post-training, and multimodal evaluation and data work.