Machine Learning Engineer (Physics-based)

🏢 Root Access · 📍 New York City · 💼 onsite · ⏰ FullTime

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Position Summary & Responsibilities

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.

🎯 Candidate Preparation Guide For Root Access Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Deep technical depth in cybersecurity, infrastructure access management, or developer tooling, evaluated through threat-modeling and architectural rigor.

📋 Preparation Priorities (Key Areas to Clarify):

1. Treating security as a compliance checkbox rather than an operational engineering discipline
2. Proposing security controls that completely bottleneck engineering velocity
3. Vague understanding of identity and access management (IAM) or cryptographic primitives

✨ Recommended Role Alignment Hook:

I engineer rock-solid security architectures that protect critical infrastructure while actually making developers' lives faster and easier.

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