L

Lambda

Cloud-based GPU services for AI training

Hardware Quality Engineer

Quality EngineeringConfirmed live in the last 24 hours
Full-timeSan Jose, CA, USA

About the Job

Requirements

  • Experience working with hardware, data center, or infrastructure systems.
  • Strong data analysis, statistics, and metrics skills, including turning raw data into insights.
  • Skill in root cause analysis methods such as 5 Whys, fishbone, 8D, and A3.
  • Ability to manage cross-team communication, stakeholder expectations, and conflict resolution.
  • Detail-oriented, process-driven, and quality-minded approach.
  • Experience working with quality tools or quality management system software, such as audit modules, enterprise resource planning systems, or defect tracking.
  • Clear written and verbal communication in English.

Responsibilities

  • Track, log, and manage all quality issues arising in the data center during deployment and production.
  • Perform root cause analysis for every hardware, software, and process failure.
  • Analyze production system metrics and quality data to detect trends, anomalies, or weak points.
  • Improve turnaround time for Return Merchandise Authorization processes.
  • Design, monitor, and drive corrective and preventive actions.
  • Implement and verify containment actions to keep systems operational until permanent fixes are applied.
  • Collaborate with operations, hardware, engineering, supply chain, and vendors to resolve quality issues.
  • Capture and upload failure analysis reports and related data into quality management systems.
  • Verify the quality of incoming and outgoing spares to avoid repeat failures.
  • Define and track quality key performance indicators and service-level agreements, and report quality performance to leadership.
  • Oversee Material Review Board inventory, rework, and disposal decisions.
  • Ensure the quality management system is up to date and necessary training is rolled out.
  • Work cross-functionally during hardware ramp, deployments, and upgrades to ensure quality gates.
  • Travel up to 30% as required.

Desired Qualifications

  • Experience in the machine learning, AI infrastructure, GPU, high-performance computing, or computer hardware industry.
  • Exposure to data center standards and certifications such as ISO or Uptime Institute.
  • Experience with vendor quality, supply chain quality, or incoming inspections.
  • Understanding of firmware, embedded systems, and reliability engineering.
  • Familiarity with scripting or automation such as Python or SQL for data processing.
  • Exposure to cloud or hyperscaler infrastructure operations.
  • Experience with manufacturing-like quality concepts applied to compute hardware.

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