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 assistanceCreate training, fine-tuning, and evaluation pipelines for PCB and schematic dataDevelop model-driven tooling that integrates into production design workflowsWork with PCB, CAD, and product teams to encode design rules and constraints into model behaviorPrototype and productionize systems for retrieval, ranking, generation, scoring, and design-space explorationBuild feedback loops between model outputs, design data, and user actionsDefine and improve metrics for layout quality, constraint satisfaction, manufacturability, and user valueOwn 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 fieldStrong background in machine learning and/or electrical engineering5+ years of relevant industry experienceStrong 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 systemsExperience working with AWS and/or GCP for model training, data pipelines, or deploymentAbility to work with structured, geometric, graph-based, or optimization-heavy problemsComfort working on real-world systems shaped by physical constraintsAbility to build production-facing tools, pipelines, and systems Preferred Qualifications Master's degree or higher in a relevant fieldDeep familiarity with PCB design, circuit behavior, layout constraints, or manufacturabilityExperience with EDA, CAD, routing, placement, optimization, simulation, or embedded systemsExperience applying ML to physical systems or hardware-aware problemsExperience with GPU-based training workflows, distributed training, or model serving infrastructureStrong experimental judgment and ability to define useful evaluation metricsComfort in a startup environment with high ownership and fast iteration