Research Engineer (Materials Foundation Models)

About the role

ABOUT CUSPAI

CuspAI is the frontier AI company on a mission to solve the breakthrough materials needed to power human progress. While nature took billions of years to perfect molecules, we are harnessing AI to unlock trillion-dollar materials breakthroughs in months, not millennia. Our founding team is the most cited in the world, comprised of world-class researchers in AI, chemistry and engineering.

We are working on some of the hardest and most important challenges including energy, clean water, the future of compute, and carbon capture, and this is just the start of what our 'search engine' for next-generation materials will unlock.

We invite you to be part of a diverse, innovative team at the intersection of AI and materials science, working to create impactful partnerships that drive innovation, scalability, and industry collaboration. This work matters. Your work matters.

We’re on the cusp of the on-demand materials era. Join us.

THE ROLE

To scale our scientific intelligence and data curation capabilities, we are looking for three Research Engineers* to train and evaluate the foundation models behind our materials discovery platform: representation learning over crystal structures and properties, LLM fine-tuning and post-training for scientific tasks, and the evaluation and data work that makes those models useful. You will work in a small team where each person owns a model end to end, from data and experiment design through training at scale to integration.

*Note that you would be joining as a ‘Member of Technical Staff’, but the job title above hopefully helps to explain the nature of this role.

YOUR IMPACT

You will be responsible for building and training specialised foundation models on terabytes of scientific data at massive scale, fundamentally enhancing our platform's multi-modal capabilities.

The modalities span crystal structures, properties, text and images. Depending on your background you may lead on self-supervised representation learning, LLM fine-tuning and post-training (SFT, RL), or the evaluation suites that decide between them.

WHAT YOU WILL DO

Research and Model Training at Scale

- Design and run experiments: scaling studies, ablations and evaluations that settle which modelling choices survive.

- Lead efforts in parallel training of multi-modal foundation models at scale (e.g. orchestrating parallel training across hundreds of GPUs and handling data in the order of Terabytes).

- Apply strong software engineering best practices to ensure the systems are scalable, reliable, and maintainable.

- Own the data curation and evaluation design for your models; these outlast the modelling decisions.

Scientific Collaboration & Integration

- Partner closely with our Chemists and Materials Scientists to integrate computational and experimental workflows into one seamless optimisation loop.