Applied ML Researcher (Force Fields and Simulation)

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.

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 Qualifications:

• You are motivated by the opportunity to build foundational tools and infrastructure that enable scientists to work on world-changing challenges.

• Demonstrated technical excellence in both research and implementation; you have a track record of building high-quality, performant systems rather than just writing theoretical papers.

• Exceptional coding skills with a strong command of modern software engineering practices.

• Deep production or research experience with distributed machine learning systems.

• PhD (or comparable professional experience) in a relevant quantitative field (e.g. Computer Science, Physics, Applied Mathematics, Computational Science, Machine Learning) with a strong foundation in computational methods.

• A genuine and explicit interest in the potential applications of AI within materials science and chemistry.

Bonus Points (But Not Critical):

• Experience with deploying, training, and modifying machine learning force fields. Note: this is a strong bonus, but not required for exceptional candidates.

• Experience with management of atomistic data.

• Experience with Density Functional Theory.

• Experience with molecular simulation methods (MCMC, MD).

• Experience with graph neural network design.

• Experience with Cloud infrastructure and Kubernetes.

• A track record of published research at top-tier venues in ML (e.g. NeurIPS, ICML) or computational physics.