Root Access
Machine Learning Engineer
New York City
Sponsorship not specifiedDetected 11 days ago
PythonAlgorithmsMachine LearningDeep LearningPyTorchNumPyData EngineeringLLMsPCB Design
About the role
- About the company Root Access is a frontier electronics company.
- Our team is a passionate mix of engineers across electrical, firmware, software, and machine learning.
Responsibilities
- Develop high-performance asset pipelines to convert geometric, discrete, and multi-layer PCB files (ODB++, IPC-2581, STEP, Gerber) into continuous space data.
- Collaborate on connecting upstream Graph Neural Networks (GNNs) or LLMs mapping schematic topologies to downstream spatial physics engines.
Requirements
- Required Technical Skills & Qualifications
- 4+ years of expert-level experience with PyTorch or JAX.
- Strong proficiency in manipulating spatial or geometric datasets using Python libraries (NumPy, SciPy, Shapely, Open3D, or custom voxelization matrices).
Benefits
- Design and train deep learning models.
Company info
- Root Access is a frontier electronics company.
- We are a NYC-based startup funded by top investors.
- Core Responsibilities
- Architect Physics Foundation Models: Design and train deep learning models.
- 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 space data.
- 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.
- 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, FNOs, etc.
- 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).
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