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Responsibilities

• Architect Physics Foundation Models: Design and train deep learning models—specifically PINNs, FNOs, and Neural Operators—optimized to solve Maxwell’s equations, Helmholtz equations, and heat equations directly within the neural loss function.
• Build the ECAD Data Pipeline: Develop high-performance asset pipelines to convert geometric, discrete, and multi-layer PCB files (ODB++, IPC-2581, STEP, Gerber) into continuous tensor grids, signed distance fields (SDFs), or graph embeddings.
• Close the Simulation-to-Reality (Sim2Real) Gap: Implement Differentiable Physics Calibration pipelines to ingest physical lab measurements (VNA Touchstone files, TDR traces, near-field EMI scans) to fine-tune latent material and manufacturing parameters.
• Multi-Modal Architecture Integration: Collaborate on connecting upstream Graph Neural Networks (GNNs) or LLMs mapping schematic topologies to downstream spatial physics engines.
• Optimize for Real-Time Execution: Optimize training and inference pipelines on GPU clusters to ensure forward-pass physics predictions can execute in sub-100 millisecond timeframes, enabling real-time feedback loops for layout designers.

Requirements

• Master’s or Ph.D. in Computer Science, Mathematics, EE, Physics, or a related quantitative field with a focus on Scientific Machine Learning (SciML).
• 4+ years of expert-level experience with PyTorch or JAX.
• Direct, hands-on experience building and training PINNs, DeepONets, or Fourier Neural Operators (FNOs).
• Direct experience using frameworks like NVIDIA Modulus, DeepXDE, or PyTorch Geometric.
• Exceptional understanding of partial differential equations (PDEs), vector calculus, automatic differentiation (autograd), and numerical optimization algorithms (Adam, L-BFGS).
• Strong proficiency in manipulating spatial or geometric datasets using Python libraries (NumPy, SciPy, Shapely, Open3D, or custom voxelization matrices).

Skills

• PyTorch
• JAX
• PINNs
• DeepONets
• Fourier Neural Operators (FNOs)
• NVIDIA Modulus
• DeepXDE
• PyTorch Geometric
• Partial Differential Equations (PDEs)
• Vector Calculus
• Automatic Differentiation (autograd)
• Numerical Optimization Algorithms (Adam, L-BFGS)
• Python
• NumPy
• SciPy
• Shapely
• Open3D
• Voxelization Matrices

Location

• NYC-based

Experience Level

• 4+ years of expert-level experience

Education Level

• Master’s or Ph.D.

About the Company

• Root Access is a frontier electronics company.
• We are a NYC-based startup funded by top investors.
• Our team is a passionate mix of engineers across electrical, firmware, software, and machine learning.