Mirror Physics
AI Researcher: Physics Foundation Models
New York City · Junior
H1B sponsorship availableDetected 407 days ago
PythonAlgorithmsMachine LearningDeep LearningPyTorchNLPCFDResearchCommunicationCollaboration
About the role
- The engine of the Mirror platform is our foundation model for physics-based simulation.
Responsibilities
- Develop robust, scalable, and generalizable atomistic models with high fidelity across chemical domains.
- develop new objectives that maximize data efficiency.
- Develop diagnostic tooling for model performance, failure-mode analysis, and uncertainty quantification
- Mentor junior researchers and collaborate with applied science and engineering teams.
- Curate heterogeneous and multi-fidelity datasets into unified training corpora; develop new objectives that maximize data efficiency.
- Develop diagnostic tooling for model performance, failure-mode analysis, and uncertainty quantification; propose new benchmarks that stress-test predictive accuracy, physical consistency, and extrapolation.
Requirements
- Familiarity with JAX.
Nice to have
- Fluency in Python plus PyTorch and familiarity with distributed training tooling (CUDA, NCCL, Slurm).
- Preferred Extras
Compensation
- Competitive salary + meaningful equity
Benefits
- Competitive salary + meaningful equity
- Full health, dental, and vision benefits for you and your family
- Personal fitness budget
- Unlimited PTO and all national holidays
- Ph.D. or M.S./B.S. with equivalent research record in Physics, Materials Science, Computer Science, or related field with a strong emphasis on machine learning and atomistic modeling.
- 3+ years experience with deep learning at scale, especially equivariant GNNs, diffusion and transformer architectures.
Equal opportunity
- Mirror is an equal-opportunity employer.
- We celebrate diversity and are committed to creating an inclusive environment for all employees.
Visa & Work Authorization
- Visa sponsorship available.
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This listing is sourced directly from Mirror Physics's careers page and normalized into a canonical job model.