Machine Learning Engineer

Total Comp: Base + Equity + Bonus, Standard Benefits

About the company

This is a frontier deep tech company: a NYC-based startup backed by top-tier investors, building at the intersection of machine learning, physics, and advanced electronics. The team blends engineers across electrical, firmware, software, and machine learning, all working on technology designed to bridge the gap between AI and the physical world.

About the role

We're recruiting a Machine Learning Engineer to work on the development of physics foundation models and the infrastructure behind them. You'll be tackling highly technical problems across deep learning, scientific computing, geometry, and physics, building models that can learn from complex physical systems and engineering data.

You'll work closely with a small team of exceptional engineers, taking ideas from research through to working systems. This is a build fast, solve hard problems kind of role, ideal for an ML engineer who wants to work on problems that sit well beyond conventional AI applications.

Key responsibilities

• Design and train deep learning models for physics foundation model applications
• Develop high performance data pipelines that convert geometric, discrete, and multi-layer engineering data into continuous spatial representations
• Work with complex engineering file formats including ODB++, IPC-2581, STEP, Gerber, and related datasets
• Collaborate on connecting upstream Graph Neural Networks or LLMs that map schematic topologies to downstream spatial physics engines
• Develop representations and processing pipelines for complex spatial and geometric datasets
• Build and optimise training and inference pipelines running on GPU clusters
• Experiment with Scientific Machine Learning approaches including Physics Informed Neural Networks (PINNs), Fourier Neural Operators (FNOs), and related architectures
• Work closely with software, electrical, firmware, and machine learning engineers throughout the development process
• Optimise models and inference systems for real-time execution and computational efficiency

Ideal profile

• Master's or Ph.D. in Computer Science, Mathematics, Electrical Engineering, Physics, or a related quantitative field with a focus on Scientific Machine Learning
• 4+ years of expert-level experience with PyTorch or JAX
• Direct, hands‑on experience building and training PINNs, FNOs, or similar Scientific ML architectures
• Exceptional understanding of partial differential equations, vector calculus, automatic differentiation, and numerical optimisation
• Strong understanding of optimisation algorithms such as Adam and L-BFGS
• Strong Python skills and experience working with scientific computing libraries such as NumPy and SciPy
• Experience manipulating spatial, geometric, or other complex structured datasets

Preferred qualifications

• Experience with Graph Neural Networks, LLMs, neural operators, or other multi-modal architectures
• Experience working with engineering, CAD, PCB, simulation, or other physical system datasets
• Proficiency with Shapely, Open3D, voxelisation techniques, or custom spatial matrices
• Experience working with GPU clusters and large-scale model training
• Experience optimising machine learning models for real-time or low-latency inference
• Background in computational physics, numerical methods, computational geometry, or simulation
• Experience connecting multiple ML architectures into a larger end-to-end system
• Experience taking research concepts through to production