# Machine Learning Engineer (Physics-based) at Root Access

> Onsite · United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Aroot-access%3A6ab61b1c-b92d-4189-86fc-9dc98d5a69ce (read 2026-11-10).

## Facts (observed)

- Company: [Root Access](https://alderhq.app/companies/root-access)
- Location text: New York City
- Where it can be done from (inferred): Onsite · United States (evidence: "Location: New York City")
- Posted: 2026-10-12 (29 days ago)
- First seen by alder: 2026-11-07 · last verified: 2026-11-07
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/root-access/6ab61b1c-b92d-4189-86fc-9dc98d5a69ce
- Skills listed (inferred): Python, PyTorch

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (New York, n 90): p25 $177k, median $233k, p75 $275k

## Description (the employer's text)

### 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.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aroot-access%3A6ab61b1c-b92d-4189-86fc-9dc98d5a69ce and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.