Machine Learning Engineer

I’m working on a particularly interesting Machine Learning Engineer opportunity with an innovative engineering technology business in New York.

This isn’t another LLM integration or chatbot development role.

The focus is on using machine learning to solve difficult real-world engineering problems, combining physics-based simulation, numerical modelling and advanced ML techniques to improve how complex electronic systems are designed and evaluated.

You’ll work at the intersection of machine learning, computational science and engineering, developing models that can predict physical behaviour and dramatically reduce the time required for traditional simulations.

What you’ll be doing:

• 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.

Machine Learning, Machine Learning, Conversation Engine, Simulation, Physics, Electronic Design, Time Management, Computational Engineering, Predictive Modeling, Electromagnetism, * 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.
• A 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 have:

• 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.

• Base salary: $150,000-$180,000
• Equity + bonus
• New York City, 5 days per week on-site

This would suit an engineer who enjoys working on challenging research-driven problems but wants to see their machine learning work applied to real products and practical engineering challenges.

If your background combines machine learning, physics and software engineering, I’d be keen to have a conversation.