Applied ML Engineer, EDA & Physical AI

Buildwithtrace; San Francisco (CA); Hybrid; USD 160,000 - 220,000; Full time

Job summary

Buildwithtrace seeks a senior ML/EE engineer in the San Francisco Bay Area or remote. You will design and deploy neural networks and LLM-enhanced systems to improve PCB design, routing, and layout decisions.

You will build training pipelines, collaborate with hardware/EDA teams, and own end-to-end projects from data prep to production. Strong ML/EE background and 5+ years of experience are required.

Qualifications

• Bachelor's degree or higher in ML, EE, CE, CS, Mathematics, Physics, or related quantitative field.
• 5+ years of relevant industry experience.
• Strong background in machine learning and/or electrical engineering.
• Proficiency in Python and ML frameworks (PyTorch or TensorFlow).
• Experience training, fine-tuning, evaluating, or deploying neural networks or LLMs.
• Experience with AWS and/or GCP for model training, data pipelines, or deployment.

Responsibilities

• 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.

Skills

Python

PyTorch

TensorFlow

Neural networks

Electrical engineering

ML/EE background

AWS

GCP

Optimization

Production tooling

Education

Bachelor's degree or higher in ML/EE/CE/CS/Math/Physics

Tools

AWS

GCP

Job description

Full-Time San Francisco Bay Area or Remote Engineering $160,000–$220,000 + equity

5+ years preferred Master's or higher preferred; degree in ML, EE, CE, CS, Math, Physics, or related

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