Senior Thermal Solutions Design Engineer

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

This role focuses on building and deploying thermal solutions for next-gen GPU/SOC products, incorporating AI-enabled approaches and tools to accelerate design iteration and validation. The engineer will collaborate with AI/tooling teams as a domain SME and apply data analytics/ML concepts to characterization data, but will not be building AI models or infrastructure.

What you'd actually do

  1. Build thermal solutions for engineering characterization and validation of next-gen GPU/SOC products, ensuring flawless delivery from concept to lab.
  2. Drive end-to-end development and deployment of thermal solutions, collaborating with internal teams and external vendors on build requirements, prototype evaluation, test system integration, and software automation.
  3. Improve thermal design processes by incorporating feedback and findings, developing workflow and maintaining our world-class standards.
  4. Work closely with system architects, chip and board designers, and software/firmware engineers in a dynamic and high-energy environment to bring industry-defining products to market.
  5. Apply AI-enabled approaches and AI tools to accelerate design iteration, test planning, and characterization/validation triage (e.g., requirements/spec summarization, experiment prioritization, log/telemetry summarization, anomaly/outlier detection), improving cycle time, coverage, and traceability while validating outputs against physics, specs, and lab measurements.

Skills

Required

  • Bachelor/Masters in Electrical Engineering, Thermal/Energy, Mechanical Engineering, or related majors (or equivalent experience), with 8+ years of relevant experience.
  • Strong understanding of thermal management principles and fluid dynamics.
  • In-depth knowledge of sophisticated cooling solutions and high heat flux problems.
  • Outstanding communication and program management skills.
  • Proficiency in CAD software for crafting and modifying 3D models of electronic components and cooling systems.
  • Solid understanding of silicon testing, board build, and software knowledge.
  • Comfort adopting AI-assisted engineering tools responsibly (effective prompting, verification/traceability, and data/IP hygiene) to improve productivity

Nice to have

  • Experience working with external vendors on building and deployment.
  • Familiarity with statistical methods and tools for data analysis.
  • Experience applying modern data analytics/ML concepts to characterization data (trend analysis, outlier detection, regression/surrogate modeling, or experiment design/optimization) to reduce debug time and improve coverage.

What the JD emphasized

  • without expectation of building AI agent infrastructure or training models