Machine Learning Engineer

Refactor.•New York City, NY

23d•Onsite

About The Position

This Machine Learning Engineer role focuses on applying machine learning to real-world engineering problems, combining physics-based simulation, numerical modelling, and advanced ML to improve the design and evaluation of complex electronic systems. The engineer will develop models that predict physical behaviour and reduce the time required for traditional simulations. The role is based in New York City and requires working on-site five days per week. Compensation is a base salary of $150,000–$180,000, plus equity and bonus.

Requirements

Strong Python programming skills.
Hands-on experience with PyTorch, JAX, or similar machine learning frameworks.
Experience developing machine learning models for scientific, physics-based, or engineering applications.
Strong understanding of numerical methods, computational modelling, or applied mathematics.
Experience working with simulation tools, numerical solvers, or scientific datasets.
Ability to develop reliable ML pipelines and production-quality software.

Nice To Haves

A PhD in Physics, Applied Mathematics, Electrical Engineering, Computer Science, or a related discipline.
Experience with physics-informed neural networks, neural operators, or surrogate modelling.
Knowledge of scientific computing, computational electromagnetics, or engineering optimisation.

Responsibilities

Develop machine learning models that predict the behaviour and performance of complex electronic systems.
Build surrogate models that approximate computationally expensive physics simulations.
Work with electrical, thermal, and electromagnetic simulation data.
Develop automated pipelines for generating, processing, and validating engineering datasets.
Apply physics-informed machine learning and numerical modelling techniques.
Build optimisation and active learning workflows to improve engineering design processes.
Validate model predictions against simulation outputs and experimental measurements.
Take ownership of ML training, evaluation, reproducibility, and deployment workflows.