L

Lambda

Cloud-based GPU services for AI training

Staff Mechanical Engineer

Building Services EngineeringConfirmed live in the last 24 hours
Full-timeHybrid

About the Job

Requirements

  • Experience designing mechanical systems for data centers, mission-critical facilities, central plants, or large industrial environments.
  • Understanding of hydronic systems, heat transfer, chilled-water systems, heat rejection, pumping, filtration, and water treatment.
  • Ability to develop and review P&IDs, mechanical layouts, equipment schedules, technical specifications, and engineering calculations.
  • Understanding of mechanical redundancy, failure domains, maintainability, and operational requirements.
  • Ability to evaluate system performance across changing weather conditions and compute loads.
  • Understanding of applicable mechanical, plumbing, energy, and safety codes and standards.
  • Ability to balance cooling performance, efficiency, water consumption, reliability, cost, and delivery schedule.
  • Bachelor’s degree in mechanical engineering or equivalent practical experience.

Responsibilities

  • Own mechanical design standards and reference architectures for large-scale AI data centers.
  • Develop cooling architectures for greenfield, retrofit, and partner-operated facilities.
  • Design central plants, heat-rejection systems, hydronic distribution, air-cooling systems, and direct-to-chip liquid-cooling infrastructure.
  • Perform cooling-load, heat-transfer, hydraulic, pressure-drop, psychrometric, and equipment-capacity calculations.
  • Develop and review P&IDs, equipment schedules, layouts, specifications, sequences of operation, and basis-of-design documents.
  • Select and size chillers, cooling towers, dry coolers, pumps, heat exchangers, CRAHs, CDUs, piping, valves, and water-treatment systems.
  • Evaluate designs for capacity, redundancy, failure isolation, maintainability, efficiency, water use, constructability, cost, and schedule.
  • Translate GPU, rack, and cluster thermal requirements into facility cooling requirements.
  • Review partner designs, vendor submittals, construction documents, and equipment packages for compliance with Lambda standards.
  • Coordinate mechanical interfaces with electrical systems, controls, building structure, hardware engineering, construction, commissioning, and operations.
  • Support equipment selection, long-lead procurement, factory testing, construction sequencing, commissioning, and system bring-up.
  • Investigate cooling failures and operational risks, and apply lessons to future designs.
  • Travel to project sites, manufacturers, and partner facilities as needed.

Desired Qualifications

  • Experience designing hyperscale, colocation, high-performance computing, or AI data centers.
  • Experience with high-density GPU infrastructure and direct-to-chip liquid cooling.
  • Experience with CDUs, warm-water cooling, dry coolers, adiabatic systems, or water-side economization.
  • Familiarity with computational fluid dynamics, Pipe-Flo, AFT Fathom, Revit, or comparable engineering tools.
  • Experience with water chemistry, glycol systems, filtration, flushing, and cooling-system cleanliness requirements.
  • Experience supporting factory testing, commissioning, integrated systems testing, or failure investigations.
  • Professional Engineer licensure or progress toward licensure.

About the Company

Lambda

Cloud-based GPU services for AI training

Lambda Labs provides cloud-based GPU services for AI training and inference via its AI Developer Cloud, powered by NVIDIA GH200 Grace Hopper hardware to efficiently train large language models and generative AI. Customers access on-demand or reserved GPUs and are billed by the hour (for example, NVIDIA H100 at about $1.99/hour); it also offers Lambda Echelon, where clients own infrastructure but pay a monthly hosting and support fee, and Lambda Stack, a software suite that helps ML teams install and upgrade tools like PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA drivers. The company differentiates itself through competitive pricing, available high-end hardware, and an integrated ML software stack that streamlines workflows. Its goal is to enable scalable, affordable AI development and deployment by providing flexible GPU access, reliable hosting, and easy software deployment for teams working with large models.

Company size
501-1,000
Company stage
Late Stage VC
Total funding
$9.1B
Headquarters
San Jose, CA
Founded
2012

Recently Posted Jobs

Explore more opportunities at Lambda

See more jobs