Machine Learning Engineer (Physics-based)

Visible Hands; New York (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

Visible Hands is seeking 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 physics-grounded models for faster, more reliable design.

You will work across hardware and ML teams to turn simulations and measurements into tooling that accelerates electronics development, emphasizing Python and modern ML frameworks.

Qualifications

• Proficient in Python for scientific computing and ML.
• Experience building ML models for engineering applications.
• Strong grounding in numerical methods and physics-based modeling.
• Experience automating simulation tools or software pipelines.
• Ability to work with large engineering datasets and production code.

Responsibilities

• Build and scale automated workflows for electrical, thermal, and electromagnetic simulations.
• Develop surrogate models to approximate physics simulations with faster inference.
• Create training datasets from simulations and hardware measurements.
• Incorporate physical constraints and engineering knowledge into models.
• Validate model predictions against solvers and experiments.
• Develop active learning and optimization to prioritize simulations.
• Own reproducible pipelines for data generation, model training, evaluation, and deployment.

Skills

Python programming

ML model development

Numerical methods

Data pipelines

Physics-informed ML

Education

Bachelor's or Master's in physics, applied mathematics, computer science, electrical engineering, or related field

PhD in simulations / surrogate models / physics-informed ML (preferred)

Tools

PyTorch

JAX

Job description

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.