Machine Learning Engineer
Basis Set · Menlo Park, CA · 2 wk ago
EngineeringFull-time
Menlo Park, CA, USA
Posted on Aug 11, 2026
About the role
Build and scale neural operator architectures for physics simulation. A rare opportunity to push the frontier of pre-training.
Responsibilities
- Design, implement, and optimize neural operator models for large-scale physics-based simulations.
- Develop and refine pre-training methodologies to improve model generalization and efficiency.
- Collaborate with cross-functional teams to integrate models into production systems.
- Conduct research to advance the state-of-the-art in neural operators and physics-informed machine learning.
- Publish findings and contribute to the broader machine learning and scientific communities.
Requirements
- Advanced degree (PhD preferred) in Machine Learning, Computer Science, Applied Mathematics, Physics, or a related field.
- Strong background in deep learning, particularly in neural operators, transformers, or related architectures.
- Experience with physics-informed machine learning, scientific computing, or simulation-based modeling.
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Track record of publishing research in top-tier conferences or journals (e.g., NeurIPS, ICML, ICLR, JMLR).
- Ability to work in a fast-paced, collaborative environment with a focus on impactful research.