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Root Access is hiring a Machine Learning Engineer to develop models that predict and optimize the behavior of electronic systems. You will work at the intersection of physics simulation and machine learning, building tools that accelerate electronics development.
In this role, you will:
- Build and scale automated workflows for electrical, thermal, and electromagnetic simulations
- Develop surrogate models that approximate physics simulations with faster inference, enabling rapid design iteration
- Create training datasets from both simulations and hardware measurements
- Incorporate physical constraints and engineering knowledge directly into model architectures, training procedures, and evaluation metrics
- Validate model predictions against established solvers and experimental results to ensure accuracy
- Develop active learning and optimization workflows to identify promising designs and intelligently prioritize which simulations to run
- Own reproducible end-to-end pipelines for data generation, model training, evaluation, and deployment
You will collaborate closely with hardware and machine learning teams to translate engineering simulations and physical measurements into practical tools.
QUALIFICATIONS:
- Strong Python skills and hands-on experience with PyTorch, JAX, or similar ML frameworks
- Demonstrated 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 professional experience)
- Preferred: PhD with experience in simulations, surrogate models, neural operators, or physics-informed machine learning
