Machine Learning Engineer (Physics-based)
Root Access · New York City ·
- Work mode
- Onsite
- Employment
- Full time
- Category
- ML ai
Build machine learning models that predict and optimize the behavior of electronic systems, grounded in electrical, thermal, and electromagnetic physics. Day to day: automating simulation pipelines, creating training datasets from simulations and hardware measurements, and developing physics-informed surrogate models using Python and frameworks like PyTorch or JAX.
Role Description
Root Access is hiring a Machine Learning Engineer to develop models that predict and optimize the behavior of electronic systems. You will build automated simulation pipelines, generate training datasets, and develop models grounded in electrical, thermal, and electromagnetic physics. Working across our hardware and machine learning teams, you will turn engineering simulations and physical measurements into tools that accelerate electronics development.
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.
Qualifications
Strong Python skills and experience with PyTorch, JAX, or similar ML 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.