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Machine Learning Engineer
Root Access
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About this role
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.
Core 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.
Required Technical Skills & Qualifications
- Education: Master’s or Ph.D. in Computer Science, Mathematics, EE, Physics, or a related quantitative field with a focus on Scientific Machine Learning (SciML).
- Deep Learning Frameworks: 4+ years of expert-level experience with PyTorch or JAX.
- SciML Expertise: 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.
- Mathematical Depth: Exceptional understanding of partial differential equations (PDEs), vector calculus, automatic differentiation (autograd), and numerical optimization algorithms (Adam, L-BFGS).
- Data Pipelines: Strong proficiency in manipulating spatial or geometric datasets using Python libraries (NumPy, SciPy, Shapely, Open3D, or custom voxelization matrices).
Salary insight
This posting doesn't disclose pay. Across 10,032 New York jobs with disclosed salaries on ForgeApply, the median is $160k.
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Based on live postings with disclosed pay on ForgeApply; refreshed daily. Not an estimate of this employer's offer.
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