Product Quality Engineer

Join Razer, a global gaming technology company, on a mission to revolutionize gaming. You will be part of a global team across 5 continents, working in a gamer-centric environment that fosters personal and professional growth.

Job Responsibilities:

• Product Quality & Reliability Qualification: Execute quality and reliability qualifications for new product introductions (NPI), ensuring tests meet defined standards, and support milestone readiness through structured quality reviews.
• Data Analysis & AI-Supported Quality Improvement: Analyze test data, failure trends, and product performance metrics using AI/ML-driven tools to enhance defect detection and predictive risk analysis.
• Issue Resolution & Root-Cause Analysis: Perform detailed failure analysis with Engineering, drive corrective & preventive actions (CAPA), and ensure timely containment and resolution of quality issues.
• Process & Documentation Management: Maintain and update quality test plans, process documentation, and qualification standards, supporting continuous improvement of quality workflows.
• Cross-Functional Collaboration: Partner with R&D, Manufacturing, Supplier Quality, and Customer Support teams on quality improvements, and communicate findings and risks clearly to stakeholders.
• Supplier & External Lab Support: Coordinate with external test labs, validate supplier test results, and support supplier quality improvements.

Pre-Requisites:

• Educational & Technical Background: Degree in Engineering (Electrical, Mechanical, Electronics, Computer, Mechatronics, or related) with a strong foundation in quality engineering, reliability engineering, or product validation/testing.
• Professional Experience: 2-5 years of experience in product quality, reliability engineering, validation, or hardware development, with experience in NPI builds, reliability qualification, or mass-production quality support.
• AI, Analytics & Tools: Ability to work with data-analysis tools (e.g., Excel, Power BI), exposure to AI/ML-based analytics or predictive modeling tools, and knowledge of scripting/programming (Python, R, Linux/Windows).
• Soft Skills & Competencies: Strong analytical, problem-solving, and troubleshooting skills, good communication skills, and high attention to detail, ownership, and a continuous-improvement mindset.