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