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# Applied AI Engineer, AI Hardware

- **Company:** Tesla Motors
- **Location:** Palo Alto, CA, United States
- **Salary:** $160,000.0 - $312,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Very-Large-Scale Integration, Artificial Neural Networks, Computer Engineering, Electronic Design Automation, Perl (Programming Language), Hardware Design, Python (Programming Language), Rapid Prototyping Process, Cadence Virtuoso, Tensorflow, Static Timing Analysis, Management of Software Versions, Pytorch, Hardware Acceleration, Machine Learning Operations, Data Pipelines
- **Published:** September 5, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/85377271/1

## About the Role

* Degree inComputer Science,Electrical or Computer Engineering, AppliedMath/Physics, or related field, or equivalent experience withexperiencedeveloping and shipping production ML systems
 * ExpertiseinML fundamentals with implementation experience inPython + ML frameworks (PyTorch, TensorFlow, JAX); experience with graph ML (e.g.,PyG, DGL) for netlists and geometric data
 * VLSI foundation in analog/digital circuit design, including transistor-level modeling, layoutparasitics, and custom ICflows isa bonus
 * Hands-on integration with EDA tools like Cadence Virtuoso,Spectre, Synopsys HSPICE,PrimeTimeand familiarity withopportunitiesfor AI enhancement
 * Knowledge of static timing analysis, routing tools, and potential AI applications
 * Proventrack recordin data pipelines for engineering datasets: curation, augmentation, and versioning for circuit simulation outputs
 * Bias for action, experimentation mindset, and ability to thrive in ambiguous, high-stakes environments with rapid prototyping

## Description

The AI Hardware Engineering team at Tesla is transforming EDA tools through AI-driven innovation to supercharge custom silicon for Full Self-Driving and Optimus robotics. We approach EDA as an end-to-end machine intelligence platform: infusing neural networks into synthesis, placement, routing, and verification flows, where every simulation cycle yields actionable data, every constraint optimization trains smarter algorithms, and every silicon revision hones our models for unprecedented speed and efficiency in next-gen autonomous systems.
What You'll Do

 * Design and develop AI-powered applicationsranging fromverifiable environmentdesign tophysicalplacementoptimization andproxyreward exploration
 * Create or customizesolutions(e.g., inTcl, Perl, or Python) to automate EDAflows, improve efficiency, and address specific design challenges in simulation, verification, orlayout(e.g.MCP)
 * Collaborate cross-functionally with software and hardware teams to accelerate the hardware development lifecycle
 * Strive to close the gap between AI-drivenEDAand traditional sign-off ready tools


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