Senior Data Center Cooling Systems Engineer

NVIDIA Ltd.
Santa Clara, CA, United States
12 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
$133,120.0 - $249,600.0
Working hours
Regular working hours
Job source

Tech stack

Computational Fluid Dynamics Data Centers Ansys

Job description

NVIDIA is seeking a Senior Data Center Cooling Systems Engineer to help develop efficient liquid-cooling solutions for high-density computing. You will work across rack, facility, and campus infrastructure to improve cooling performance, energy use, water use, reliability, acoustics, and heat reuse.

We seek an expert engineer with expertise in one or more areas, including single-phase liquid cooling, refrigeration, pumped two-phase cooling, facility cooling, heat rejection, energy and water optimization, or waste-heat recovery. Candidates need not be knowledgeable in every field.

What You’ll Be Doing:

  • Develop and evaluate single-phase liquid-cooling and pumped refrigeration systems for high-density computing.
  • Link rack-level cooling needs with facility cooling, heat rejection, and plant controls.
  • Use engineering analysis and simulation to study thermal performance, fluid flow, pressure, controls, and system response.
  • Compare build options based on energy use, water consumption, reliability, footprint, noise, cost, and heat-reuse potential.
  • Partner with suppliers and research collaborators to advance cooling and heat-rejection products.
  • Plan tests, review results, and compare simulation predictions with measured performance.
  • Turn technical findings into build requirements, reference architectures, supplier specifications, and technology roadmaps.

Requirements

  • Master’s degree in Mechanical Engineering, Thermal Engineering, Energy Engineering, Refrigeration Engineering, or a similar area of study, with equivalent experience.
  • Over 12 years of pertinent experience in data center cooling, liquid cooling, refrigeration, thermal systems, heat rejection, or critical facilities.
  • Knowledge of thermodynamics, fluid mechanics, heat transfer, refrigeration cycles, and thermal-hydraulic systems.
  • Experience in at least one applicable field, such as direct liquid cooling, pumped refrigeration, two-phase cooling, facility cooling, heat rejection, acoustics, or waste-heat recovery.
  • Experience with cooling equipment such as coolant distribution units, chillers, pumps, heat exchangers, cooling towers, or dry coolers.
  • Experience with computational fluid dynamics or fluid-network modeling tools, such as Cadence Reality DC Build, Ansys, Siemens Simcenter STAR-CCM+, Flownex, or comparable software.
  • Ability to translate engineering analysis into practical builds, test plans, technical requirements, and implementation recommendations.
  • Clear communication skills and experience working with suppliers, research partners, and multi-functional engineering teams.

Ways to Stand Out from the Crowd:

  • PhD or equivalent experience in Mechanical Engineering, Thermal Sciences, Refrigeration, Energy Systems, or a closely related area.
  • Experience with hyperscale data centers, high-performance computing systems, or other high-density computing environments.
  • Experience with pumped two-phase refrigeration, flow boiling, advanced liquid cooling, or low-global-warming-potential refrigerants.
  • Experience addressing water use, facility noise, waste-heat recovery, or other community concerns related to data center infrastructure.
  • Contributions to standards, patents, technical publications, or industry research communities.

Benefits & conditions

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 5, and 216,000 USD - 345,000 USD for Level 6.

About the company

NVIDIA is widely considered one of the technology world’s most desirable employers. We encourage collaborative engineers who enjoy solving complex problems, learning across fields, and developing more balanced computing infrastructure.

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