Machine Learning Engineer (Physics-based)
Root Access1 day ago
New York, NY, USAMid Level
Responsibilities
- Build and scale automated workflows for electrical, thermal, and electromagnetic simulations.
- Develop surrogate models that approximate physics simulations with faster inference.
- Create training datasets from simulations and hardware measurements.
- Incorporate physical constraints and engineering knowledge into model architectures, training, and evaluation.
- Validate model predictions against established solvers and experimental results.
- Develop active learning and optimization workflows to identify promising designs and prioritize simulations.
- Own reproducible pipelines for data generation, model training, evaluation, and deployment.
Requirements
- Strong Python skills and experience with PyTorch, JAX, or similar machine learning frameworks.
- Experience developing and evaluating machine learning models for scientific or engineering applications.
- Working knowledge of numerical methods and physics-based modeling.
- Experience automating simulation tools or computational solvers.
- Ability to build reliable software pipelines and work with large engineering datasets.
- Degree in physics, applied mathematics, computer science, electrical engineering, or a related field, or equivalent experience.
- Preferred: PhD with experience in simulations, surrogate models, neural operators, or physics-informed machine learning.