Applied ML Engineer, EDA & Physical AI

This role sits at the intersection of machine learning, electronic design automation, and physical engineering systems. You will work on neural networks, LLM-powered systems, and data pipelines that improve how PCB designs are created and refined in practice. What you'll do Build and improve neural network and LLM systems for circuit understanding, placement, routing, and design assistance Create training, fine-tuning, and evaluation pipelines for PCB and schematic data Develop model-driven tooling that integrates into production design workflows Work with PCB, CAD, and product teams to encode design rules and constraints into model behavior Prototype and productionize systems for retrieval, ranking, generation, scoring, and design-space exploration Build feedback loops between model outputs, design data, and user actions Define and improve metrics for layout quality, constraint satisfaction, manufacturability, and user value Own projects end to end, from data preparation and experiments to deployment and iteration Required Qualifications Bachelor's degree or higher in ML, EE, CE, CS, Mathematics, Physics, or a related quantitative field Strong background in machine learning and/or electrical engineering 5+ years of relevant industry experience Strong experience with Python and at least one major ML framework (PyTorch or TensorFlow) Experience training, fine-tuning, evaluating, or deploying neural networks, LLMs, or similar systems Experience working with AWS and/or GCP for model training, data pipelines, or deployment Ability to work with structured, geometric, graph-based, or optimization-heavy problems Comfort working on real-world systems shaped by physical constraints Ability to build production-facing tools, pipelines, and systems Preferred Qualifications Master's degree or higher in a relevant field Deep familiarity with PCB design, circuit behavior, layout constraints, or manufacturability Experience with EDA, CAD, routing, placement, optimization, simulation, or embedded systems Experience applying ML to physical systems or hardware-aware problems Experience with GPU-based training workflows, distributed training, or model serving infrastructure Strong experimental judgment and ability to define useful evaluation metrics Comfort in a startup environment with high ownership and fast iteration #J-18808-Ljbffr Trace