Thermal Solutions Development Engineer collaborating with teams to drive thermal solution innovation for NVIDIA's next-gen products. Focusing on AI-enabled processes and cross-functional partnerships for product delivery.
Responsibilities
Build thermal solutions for engineering characterization and validation of next-gen GPU/SOC products, ensuring flawless delivery from concept to lab.
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.
Improve thermal design processes by incorporating feedback and findings, developing workflow and maintaining our world-class standards.
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.
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.
Partner with AI/tooling teams as the thermal domain SME to define use-cases, success criteria, and evaluation methods; provide feedback to improve tool reliability and usability.
Requirements
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—without expectation of building AI agent infrastructure or training models.
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