Robotic Thermal Design Engineer, Optimus

Tesla Tesla · Auto · Palo Alto, CA · Tesla AI

This role focuses on the thermal design and management of AI silicon for Tesla's Optimus humanoid robot. It involves co-designing thermal solutions, simulation, material selection, and cross-functional collaboration to ensure performance and reliability in high-volume production.

What you'd actually do

  1. AI Chip Thermal Architecture: Lead the thermal-aware co-design of the AI chip from the ground up. Define the physical integration strategy (e.g., 2.5D/3D structures, chiplet arrangements), guide the silicon floorplan, and influence the power delivery network to proactively mitigate hot spots and maximize performance within the robotic system
  2. Simulation & Validation: Build and correlate high-fidelity thermal models to predict silicon and module performance under real-world robotic operating conditions, including transient power spikes and harsh ambient environments. Validate designs through rigorous lab testing and data analysis
  3. Material & Component Selection: Characterize, select, and qualify cutting-edge thermal interface materials (TIMs), heat spreaders, and module-level thermal solutions. Drive the material roadmap to enable the thermal architecture of future product generations
  4. Cross-Functional Leadership: Drive thermal-aware silicon floor planning and design. Collaborate with silicon, package design, and cross functional teams to define architectural trade-offs between performance, cost, and mechanical reliability
  5. Manufacturing Support: Partner with manufacturing teams to resolve thermal-related yield issues, improve cost-effectiveness, and ensure seamless transitions from R&D to production

Skills

Required

  • Mechanical Engineering or related field
  • major thermal simulation tool (e.g., Flotherm, Icepak)
  • chip-level thermal design and integration
  • heat transfer principles (conduction, convection, radiation)
  • advanced silicon integration technologies (e.g., BGA, flip-chip, 2.5D/3D integration)
  • material selection for thermal applications
  • TIMs
  • phase-change materials
  • module-level thermal solutions
  • troubleshoot thermal failures
  • optimize designs for cost/performance
  • drive solutions from concept to production

Nice to have

  • thermal management for AI accelerators (GPUs, TPUs)
  • robotics
  • automotive-grade electronics
  • JEDEC/IPC standards
  • scripting (Python, MATLAB) for data analysis and automation

What the JD emphasized

  • high-volume production
  • AI Chip Thermal Architecture
  • thermal-aware co-design
  • high-density AI silicon
  • robotic system
  • real-world robotic operating conditions
  • future product generations
  • cross functional teams
  • manufacturing teams
  • thermal-related yield issues
  • AI accelerators