Applied AI Engineer, AI Hardware

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.

• Design and develop AI-powered applicationsranging from static timing analysis to physical synthesis optimization and automationenhancements
• Oversee the setup, configuration, and performance tuning of EDA tools for digital, analog, and mixed-signal design, including simulation (e.g., SPICE, Verilog simulators), synthesis, place-and-route, verification, and layout tools from vendors like Cadence, Synopsys, or Siemens
• Create or customizesolutions (e.g., inTcl, Perl, or Python) to automate EDAflows, improve efficiency, and address specific design challenges in simulation, verification, or layout
• Assistin formal verification, timing analysis, DRC/LVS checks, and ensuring designs meet fabrication standards
• Collaborate cross-functionally with software and hardware teams to accelerate the hardware development lifecycle
• Strive to close the gap between AI-drivenEDA and traditional sign-off ready tools

• Degree in Electrical or Computer Engineering, Applied Physics, or related field, or equivalent experience with 3+ years developing and shipping production ML systems for hardware, EDA, semiconductor domains or equivalent
• Strong VLSI foundation in analog/digital circuit design, including transistor-level modeling, layoutparasitics, and custom IC flows
• Expertisein Python + ML frameworks (PyTorch, TensorFlow, JAX); experience with graph ML (e.g.,PyG, DGL) for netlists and geometric data
• 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