Senior Engineer, Manufacturing Quality at QuantumScape
- Company: QuantumScape
- Location: CA, United States
- Salary: $113K – $163K
- Job type: full time
- Workplace: onsite
- Posted: 2026-10-20
All open roles at QuantumScape
Job description
QuantumScape is on a mission to transform energy storage with solid-state lithium-metal battery technology. The company’s next-generation batteries are designed to enable greater energy density, faster charging and enhanced safety to support the transition away from legacy energy sources toward a lower carbon future. About the team: Manufacturing Quality is a diverse group of engineers and data scientists who bring deep expertise in materials science, electrochemistry, statistics, and machine learning. Our team thrives on data-driven decision-making and a shared commitment to drive continuous improvement to QuantumScape’s solid-state battery technology. We work closely with the Manufacturing, Reliability, and R&D, groups to identify failure modes, refine critical-to-quality specifications, and implement robust control strategies across all process areas. What we need: We are seeking a Senior Engineer, Manufacturing Quality who has built their career on the manufacturing floor — as a process, manufacturing, yield, or quality engineer — and who uses production data to reduce variation. In this role, you will connect upstream process and metrology data to downstream yield, electrical test, and reliability outcomes, turn those correlations into specifications and control strategies, and drive them into production with process owners across QuantumScape’s cell and separator manufacturing lines. This is neither a data science role nor a quality-systems role: the ideal candidate pairs hands-on process knowledge with rigorous, practical data analysis, is passionate about optimizing tools and processes, and values consistency and documentation. If this role sounds like a good fit, we’d like to hear from you. What You’ll do: • Drive process variation down using production data: decompose variation by source (tool-to-tool, lot-to-lot, material and supplier, shift), quantify each contributor, and partner with process owners to eliminate the dominant ones, center and tighten process windows, and demonstrate sustained capability (Cpk) improvement. • Lead correlation projects that link upstream process parameters, material attributes, and in-line metrology to downstream yield, electrical test, and reliability outcomes; query and analyze the data with JMP or equivalent tools, and translate findings into concrete process, specification, and control changes on the manufacturing line. • Collaborate with process teams to identify yield improvement and defect reduction opportunities, confirm root cause through DOEs and engineering experiments, and lead improvement projects with process owners from problem definition to closure. • Develop subject-matter expertise on battery components and process steps; partner with process, equipment, and R&D engineers to define critical-to-quality (CTQ) characteristics and set specification limits grounded in process capability and product performance data. • Collaborate cross-functionally to establish repeatable measurement processes for cells and components, validated through MSA/Gage R&R, with results captured in the database and used as CTQs and specifications for process-quality tracking. Skills You’ll Need: • Bachelor’s degree in Engineering (Chemical, Mechanical, Materials Science, Electrical, Metallurgy, Industrial, or related discipline) + 5 years of experience in a process, manufacturing, yield, or quality engineering role; Master's degree + 3 years. • Hands-on experience as a process, manufacturing, yield, or quality engineer in a complex manufacturing environment (battery, semiconductor, display, data storage, or similar). • Demonstrated track record of reducing variation: isolating sources with variance components and multi-vari analysis, separating measurement from process variation, and sustaining Cpk gains. • Experience of driving correlation projects with production data and converting the results into specifications, control limits, or process changes that were implemented in production. • Stron
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