ML Research Scientist, Interatomic Potentials
Skills
- Strong programming in Python with production-quality coding practices
- Experience with ML frameworks such as PyTorch or JAX
- Hands-on experience with machine-learned interatomic potentials (training, fine-tuning, distillation)
- Practical experience running MD simulations (LAMMPS, OpenMM, or similar)
- Familiarity with HPC and cloud deployment for large-scale simulations
- Experience building reproducible scientific workflows and pipelines
Responsibilities
- Develop, fine-tune, and deploy machine-learned interatomic potentials across crystalline, amorphous, and multi-component systems.
- Build infrastructure to integrate interatomic potentials into scalable agentic frameworks for autonomous materials design.
- Link atomistic simulation workflows with high-throughput laboratory automation and collaborate with automation scientists.
- Run and validate molecular dynamics simulations using tools like LAMMPS and OpenMM and ensure reproducible workflows.
- Collaborate with materials scientists, ML researchers, and platform engineers to productionize simulation pipelines on HPC and cloud.
- Write robust, well-tested, and documented code for modeling workflows and deployment.
Education
- PhD or equivalent research/industry experience in Computational Materials Science, Computational Chemistry, Computer Science, Machine Learning, or a related field
- Strong research record or equivalent demonstrated technical impact
Benefits
- Equity participation
- Comprehensive health, dental, and vision benefits
- Flexible and hybrid work arrangements
- Generous paid time off and parental leave
- Professional development and conference support
- 401(k) or retirement plan options (where applicable)
- Inclusive, collaborative culture
Average salary estimate
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