Hardware Quality Engineer

About the role

This role focuses on maintaining high quality standards for hardware and infrastructure within Lambda's data centers. You will address quality issues, analyze failures, and implement solutions to ensure the reliability and performance of AI cloud infrastructure. This position involves close collaboration with various teams to uphold quality throughout the hardware lifecycle.

Key facts

What you'll do

• Document and track all quality problems encountered in data centers during deployment and operation.
• Conduct thorough root cause analysis for all types of failures, including hardware, software, and process-related issues.
• Examine production system metrics and quality data to identify patterns, irregularities, or weak points.
• Improve the speed of processing for Return Merchandise Authorization (RMA) requests.
• Develop, oversee, and implement corrective and preventive actions (CAPA).
• Apply and verify temporary solutions to keep systems functional until permanent fixes are available.
• Work with operations, hardware, engineering, supply chain, and external vendors to resolve quality concerns.
• Upload failure analysis reports and related information into Quality Management Systems (QMS).
• Inspect the quality of incoming and outgoing spare parts to prevent recurring failures.
• Establish and monitor quality Key Performance Indicators (KPIs) and Service Level Agreements (SLAs), reporting performance to management.
• Supervise Material Review Board (MRB) inventory, making decisions on rework or disposal.
• Keep the quality management system (QMS) current and ensure necessary training is provided.
• Collaborate across departments during hardware rollouts, deployments, and upgrades to enforce quality checkpoints.

Requirements

• Experience with hardware, data center, or infrastructure systems.
• Strong ability in data analysis, statistics, and metrics to extract insights from raw data.
• Proficiency in various root cause analysis methodologies (e.g., 5 Whys, fishbone diagrams, 8D, A3).
• Capable of managing communication across teams, stakeholder expectations, and conflict resolution.
• Possesses a strong attention to detail, is process-oriented, and prioritizes quality.
• Experience using quality tools or Quality Management System (QMS) software (e.g., audit modules, ERP, defect tracking).
• Clear communication skills in English, both written and verbal.

Nice to have

• Background in machine learning, AI infrastructure, GPU, HPC, or computer hardware industries.
• Familiarity with data center standards and certifications (e.g., ISO, Uptime Institute).
• Experience with vendor quality, supply chain quality, or incoming inspections.
• Knowledge of firmware, embedded systems, and reliability engineering.
• Familiarity with scripting or automation tools (e.g., Python, SQL) for data processing.
• Exposure to cloud or hyperscaler infrastructure operations.
• Experience applying manufacturing-like quality concepts to compute hardware.

Skills & tools

• Data analysis
• Statistics
• Root Cause Analysis (5 Whys, fishbone, 8D, A3)
• Quality Management Systems (QMS) software
• ERP
• Defect tracking
• Python (nice to have)
• SQL (nice to have)

Practical notes

• This position requires working from the San Jose office four days a week; Tuesday is currently the designated work-from-home day.
• Up to 30% travel may be necessary for this role.
• Benefits include health, dental, and vision coverage for employees and dependents, wellness and commuter stipends for specific roles, and a 401k plan with a 2% company match for US employees.
• The company offers a flexible paid time off plan.