Hardware Quality Engineer

Lambda Labs•San Jose, CA

64d•Onsite

About The Position

Lambda, The Superintelligence Cloud, builds Gigawatt-scale AI Factories for Training and Inference. Lambda's mission is to make compute as ubiquitous as electricity and give every person access to artificial intelligence. One person, one GPU. If you'd like to build the world's best deep learning cloud, join us. Note: This position requires presence in our San Jose office location 4 days per week; Lambda's designated work from home day is currently Tuesday. The Operations team plays a critical role in ensuring the seamless end-to-end execution of our AI-IaaS infrastructure and hardware. This team is responsible for sourcing all necessary infrastructure and components, overseeing day-to-day data center operations to maintain optimal performance and uptime, and driving cross company coordination through product management organization to align operational capabilities with strategic goals. By managing the full lifecycle from procurement to deployment and operational efficiency, the Operations team ensures that our AI-driven infrastructure is reliable, scalable, and aligned with business priorities.

Requirements

Have experience working with hardware / data center / infrastructure systems
Are strong at data analysis, statistics, and metrics (you can turn raw data into insight)
Are skilled in root cause analysis methods (5 Whys, fishbone, 8D, A3, etc.)
Are comfortable managing cross-team communication, stakeholder expectations, and conflict resolution
Are detail-oriented, process-driven, and quality-minded
Have experience working with quality tools or QMS software (e.g. audit modules, ERP, defect tracking)
Communicate clearly in English (both written and verbal)

Nice To Haves

Experience in the machine learning / AI infrastructure / GPU / HPC / computer hardware industry
Exposure to data center standards, certifications (e.g. ISO, Uptime Institute, etc.)
Experience working on vendor quality, supply chain quality, or incoming inspections
Understanding of firmware, embedded systems, reliability engineering
Familiarity with scripting or automation (Python, SQL, etc.) to help with data processing
Exposure to cloud or hyperscaler infrastructure operations
Experience with "manufacturing-like" quality concepts applied to compute hardware

Responsibilities

Track, log, and manage all quality issues arising in the data center during deployment and production environment
Perform root cause analysis (RCA) for every failure (hardware, software, process)
Analyze production system metrics and quality data to detect trends, anomalies, or weak points
Improve turnaround time (TAT) for Return Merchandise Authorization (RMA) processes
Design, monitor, and drive corrective and preventive actions (CAPA)
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 (FA) reports and related data into Quality Management Systems (QMS)
Verify quality of spares (incoming and outgoing) to avoid repeat failures.
Define and track quality KPIs / SLAs and report on quality performance to leadership
Oversee MRB (Material Review Board) inventory, rework, disposal decisions
Ensure the quality management system (QMS) is up to date, with necessary training rolled out
Work cross-functionally during hardware ramp, deployments, and upgrades to ensure quality gates
Up to 30% travel may be required for this role.