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 We are seeking an ML Research Engineer (Machine Learning Force Fields) to advance our molecular simulation capabilities by developing next-generation computational methods and the robust infrastructure that powers them. Note: You would be joining as a ‘Member of Technical Staff’, but the indicative job title above helps to explain the nature of this role. We are aiming to start interviewing for this role in May and would like to make an offer by the end of June. Your Impact In this role, you'll shape the simulation infrastructure that enables CuspAI to evaluate novel material candidates through atomistic physics. You'll bring these simulations to the accuracy and performance needed to power large-scale search campaigns, and design them to be flexible and versatile so they can be adapted quickly to new challenges. Your work will expand what is computationally tractable, accelerating the discovery of the breakthrough materials needed for a sustainable future. What You Will Do Models Train, fine-tune, and distill machine learning force fields. Research and develop novel ML force field architectures suited to production simulation workloads. Systems & infrastructure Integrate these models into public and in-house high-performance simulators. Develop training and inference architectures for large-scale training, data generation, and simulation. Distribute these workloads via Ray to scale across our compute infrastructure. Build the system with modularity in mind, so components can be reused across many kinds of chemistry. Science & collaboration Build an active learning system that closes the loop between simulation, data generation, and training. Develop interfaces that make the system easy for domain scientists to use and extend. Collaborate closely with computational chemists on density functional theory (DFT) data generation and validation. Must Have Skills and
