Applied AI Engineer - Hardware

Etched builds an inference-focused frontier AI platform for translating engineering intent into verified, manufacturable hardware designs. This fully in-person role in San Jose (Santana Row) pairs agentic AI tooling with deep engineering collaboration, with no hard boundaries between engineering and research. You will help ship end-to-end workflows where model evaluations, tool integrations, and simulation-driven iterations directly improve real hardware outcomes.

Compensation: USD 150,000 - 250,000 per year plus Significant Equity.

What you’ll do

• Build and own AI systems that convert engineering requirements into verified electrical and mechanical designs, carrying intent and constraints from early concepts through manufacturing outputs.
• Create agents that use CAD, EDA, and simulation tools to generate designs, run experiments, inspect results, diagnose issues, and iterate with teams to the limits of model autonomy.
• Develop workflows covering component selection, schematic capture, PCB layout, mechanical CAD, assemblies, thermal analysis, and electrical and structural simulation.
• Build the tool integrations and representations agents need to reason about geometry, connectivity, materials, tolerances, and coupled electrical, mechanical, thermal, and manufacturing constraints.
• Design evaluation methods that measure engineering correctness, constraint satisfaction, simulation accuracy, manufacturability, and performance on real design tasks.
• Convert simulation outputs, design-rule checks, engineering reviews, and physical measurements into structured feedback models that can learn from them.
• Curate proprietary datasets and design memory from complete trajectories, expert demonstrations, failed approaches, and manufactured outcomes.
• Implement reproducible experiment infrastructure so tool actions, simulation settings, and results are traceable and can run at scale.
• Ship agent-generated designs with electrical, mechanical, and manufacturing teams, and quantify improvements in design cycle time, hardware performance, and engineering effort.
• Continuously evaluate new model releases and deploy the best models and methods for each stage of the design loop.

What you bring

• A track record of solving hard problems across stacks and domains, with comfort getting dropped into unfamiliar territory.
• Hands-on experience building and shipping LLM-based agents or AI tooling people depend on, including context engineering, tool integration, orchestration, evaluation, and failure analysis.
• Strong software engineering skills, especially in Python, with the ability to build reliable integrations with complex engineering tools and direct AI to write code that ships.
• Interest, experience, or academic exposure to electrical or mechanical engineering, with the ability to reason about physical constraints and distinguish plausible designs from verified ones.
• Fluency using AI to learn and ramp on new problems, including agentic coding tools, deep research, and frontier models.
• An eval-driven mindset: measuring system performance, investigating failure modes, and using those findings to improve.
• Comfort moving across research exploration, agentic experimentation, engineering-tool debugging, and production execution.

Helpful background (nice-to-have)

• High agency and comfort with ambiguity.
• Automating CAD or EDA tools via APIs, scripting, plugins, or GUI interaction.
• Experience with schematic design, PCB layout, component selection, power delivery, or signal and power integrity.
• Parametric CAD, mechanical assemblies, tolerance analysis, thermal management, CFD, or FEA.
• Design optimization, constraint solving, or search over large engineering design spaces.
• Fine-tuning or post-training models using tool-use trajectories, simulation feedback, or expert demonstrations.
• Multimodal reasoning over engineering drawings, schematics, geometry, and simulation results.
• Experience taking hardware through fabrication, assembly, bring-up, and testing.

Technologies you’ll work with

• Python
• LLM-based agents
• CAD, EDA
• Simulation
• Agentic coding tools
• Deep research
• Transformer and transformer-like architectures

Benefits

• Medical, dental, and vision packages with generous premium coverage
• $500 per month credit for waiving medical benefits
• Housing subsidy of $2,500 per month for those living within walking distance of the office
• Relocation support for those moving to San Jose (Santana Row)
• Wellness benefits covering fitness, mental health, and more
• Daily lunch and dinner in our office
• Unlimited compute budget subject to ROI justification
• Significant equity

How Etched is different: Etched believes in the Bitter Lesson, is an inference-focused frontier AI team, runs a fully in-person organization in San Jose (Santana Row), and expects technical staff to contribute across engineering and research as needed.