Machine Learning Engineer (Physics-based)
Required skills
- Python
- PyTorch
- JAX
- Machine Learning
- scientific applications
- engineering applications
- Numerical methods
- Physics-based modeling
- Simulation automation
- Software pipelines
Nice to have
- simulations
- surrogate models
- neural operators
- physics-informed machine learning
- active learning
- optimization workflows
Role summary
Root Access is seeking a Machine Learning Engineer to develop models for predicting and optimizing electronic system behavior. This role involves building automated simulation pipelines, generating training datasets, and creating models grounded in physics. You will collaborate with hardware and ML teams to leverage engineering simulations and physical measurements, accelerating electronics development. Key responsibilities include developing surrogate models, incorporating physical constraints into ML architectures, validating predictions, and owning reproducible data generation and deployment pipelines.
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.
Similar roles
- Simulation and Software EngineerRoot Access · New York, NY · Onsite
- Electrical EngineerRoot Access · New York, NY · Onsite
Staff Machine Learning Engineer - Team Lead (Active Secret Clearance)Striveworks · Remote · Remote- Machine Learning Engineer - New GradSambaNova Systems · Austin, TX · Onsite
- Principal Machine Learning EngineerPhysicsX · New York, NY · Onsite
Staff Machine Learning Engineer, Sensors & IntelligenceTonal · Remote · Remote
