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# Machine Learning Engineer

- **Company:** LISTED INC.
- **Location:** New York, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Neural Networks, Computer Clusters, Electronic Design Automation, Python (Programming Language), Machine Learning, NumPy, SciPy, Systems Integration, Pytorch, Large Language Models, Deep Learning, Data Pipelines
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/machine-learning-engineer-root-access-8734401

## About the Role

* 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).

## Description

* 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.


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