Machine Learning Engineer

Machine Learning Engineer

New York (On-site)

Up to $150,000 pa plus equity

Help build the next generation of AI models that understand real-world physics.

I'm partnering with a well-funded, venture-backed technology startup developing advanced AI-powered hardware solutions. They're looking for a Scientific Machine Learning Engineer to help build cutting-edge machine learning models capable of solving complex physics and engineering problems.

This is a rare opportunity to join a highly technical team working at the intersection of Scientific Machine Learning, deep learning, computational physics and electronic design automation (EDA).

You'll play a key role in developing advanced AI models that learn physical behaviour, creating scalable data pipelines and collaborating with multidisciplinary engineers to deliver next-generation engineering intelligence.

What You'll Be Doing

• Design, develop and train state-of-the-art Scientific Machine Learning models.
• Build high-performance data pipelines capable of processing complex engineering and geometric datasets.
• Work with industry-standard electronic design file formats, including PCB and CAD data.
• Collaborate on integrating graph-based and transformer-based AI models with physics-aware learning systems.
• Optimise large-scale training and inference workloads for GPU environments.
• Work closely with software, machine learning and hardware engineers to develop production-ready AI solutions.

What We're Looking For

• Master's or PhD in Computer Science, Mathematics, Electrical Engineering, Physics or another highly quantitative discipline.
• 4 years of commercial experience with PyTorch or JAX.
• Hands-on experience developing Scientific Machine Learning models such as:
• Physics-Informed Neural Networks (PINNs)
• Fourier Neural Operators (FNOs)
• Other neural operators or PDE-based learning approaches
• Strong mathematical background including:
• Partial Differential Equations (PDEs)
• Vector Calculus
• Automatic Differentiation
• Numerical Optimisation (Adam, L-BFGS or similar)
• Strong Python skills with experience working on geometric or spatial datasets using libraries such as NumPy, SciPy, Shapely, Open3D or similar.

Interested?

If you're passionate about Scientific Machine Learning, computational physics or next-generation AI systems, I'd love to speak with you. Apply directly or get in touch for a confidential discussion. Company details will be shared with shortlisted candidates.

Take the next step in your career and apply today!

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