Applied ML Researcher; Molecular Simulation

Applied ML Researcher (Molecular Simulation)

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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.

Due to growth, we are seeking an Applied ML Researcher (Molecular Simulation) to advance our molecular simulation capabilities, developing next‑generation computational methods that bridge molecular‑scale physics with continuum‑level phenomena.

Hiring timelines: We’re aiming to start interviewing for this role in February and would like to make an offer by the end of March.

Simulating how molecules behave under realistic conditions is one of the key bottlenecks in materials discovery. Current tools are either too slow or too approximate, and that limits how quickly we can evaluate candidates and close the loop with experiments.

You will build the next generation of simulation methods: fast, accelerator‑native, and grounded in rigorous physics.

Your work will directly expand what is computationally tractable, letting us ask questions about molecular systems that were previously too expensive to answer.
This is foundational infrastructure for everything CuspAI does
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• Design and implement high‑performance simulation software in JAX, targeting GPU/TPU‑accelerated molecular and continuum‑scale methods
• Develop surrogate models and ML‑driven acceleration techniques for physics‑based simulations
• Build robust, reusable foundation blocks to enable the broader team to iterate rapidly on new scientific problems

• Bridge molecular simulations (MD, Monte Carlo) with continuum‑level PDE models, enabling true multiscale workflows
• Develop and refine ML‑driven enhanced sampling techniques for challenging molecular systems
• Conduct fundamental research on developing novel Machine Learning models for coarse‑graining molecular data and extracting key parameters for higher‑scale simulations

• Work closely with domain scientists across the organization to ground methods development in real project needs
• Contribute to the scientific direction of the simulation team, identifying high‑impact methodological gaps
• Translate research advances into production‑quality code

• You are motivated by the opportunity to build foundational tools that unlock new capabilities in molecular and materials science
• Demonstrated technical excellence in both research and implementation; you write high‑quality, performant code, not just papers
• Exceptional coding skills with a strong command of modern software engineering practices down to compilation level
• Deep experience with GPU/TPU‑accelerated frameworks (e.g. JAX, PyTorch) for scientific or high‑performance computing applications
• PhD (or comparable experience) in a relevant quantitative field (physics, applied mathematics, computational science, machine learning, or similar) with a strong computational mathematics foundations and the ability to apply them to physical systems
• Strong understanding of modern ML, particularly as applied to scientific problems

• Background in sampling methods (MCMC, molecular dynamics) and statistical mechanics
• Experience with molecular simulation packages or developing simulation software
• Familiarity with multiscale…