Senior Engineer, Manufacturing Quality

QuantumScape Corporation•San Jose, CA

20d•$112,500 - $163,200•Onsite

About The Position

QuantumScape is seeking a Senior Engineer, Manufacturing Quality to use hands-on manufacturing-floor experience and production data to reduce process variation. The role connects upstream process and metrology data with downstream yield, electrical test, and reliability outcomes, then turns those findings into specifications and control strategies implemented with process owners across cell and separator manufacturing lines. This is a manufacturing engineering role, rather than a data science or quality-systems role, and emphasizes practical analysis, process optimization, consistency, and documentation. The Manufacturing Quality team includes engineers and data scientists with expertise in materials science, electrochemistry, statistics, and machine learning. The team works with Manufacturing, Reliability, and R&D to identify failure modes, refine critical-to-quality specifications, and implement control strategies across process areas. The position is onsite five days per week. Work may take place in a dry room, fume hood, lab area, or glovebox and includes physical activities such as extended sitting or standing, hand reaching and twisting, and lifting up to 25 pounds.

Requirements

Bachelor’s degree in Engineering (Chemical, Mechanical, Materials Science, Electrical, Metallurgy, Industrial, or related discipline) and 5 years of experience in a process, manufacturing, yield, or quality engineering role; or a master’s degree and 3 years of experience.
Hands-on experience as a process, manufacturing, yield, or quality engineer in a complex manufacturing environment such as battery, semiconductor, display, or data storage manufacturing.
Track record of reducing variation by isolating sources with variance components and multi-vari analysis, distinguishing measurement variation from process variation, and sustaining Cpk gains.
Experience leading correlation projects with production data and converting results into specifications, control limits, or process changes implemented in production.
Applied statistics on manufacturing data using JMP or Minitab, including regression, hypothesis testing, t-tests, ANOVA, DOE, and SPC.
Working knowledge of quality tools, including structured root cause analysis (Fishbone, 5 Whys, 8D) and MSA (Gage R&R).
Excellent communication, presentation, and leadership skills, with the ability to distill insights from large data sets and influence stakeholders to improve quality and yield.
Ability to adapt to shifting priorities in a fast-paced manufacturing environment and reason from underlying physics, process mechanics, and data.

Nice To Haves

Fundamental understanding of battery electrochemistries and failure mechanisms; familiarity with electrical testing and metrics.
Six Sigma Green Belt or Black Belt, Certified Quality Engineer (CQE), or equivalent.
Experience in cleanroom or dry-room manufacturing.
Programming skills, especially in Python.

Responsibilities

Use production data to decompose process variation by source, quantify contributors, and work with process owners to eliminate dominant sources, center and tighten process windows, and demonstrate sustained Cpk improvement.
Lead analyses connecting upstream process parameters, material attributes, and in-line metrology to downstream yield, electrical test, and reliability outcomes; use JMP or equivalent tools and translate results into process, specification, and control changes on the manufacturing line.
Work with process teams to identify yield-improvement and defect-reduction opportunities, confirm root causes through DOEs and engineering experiments, and lead improvement projects from problem definition through closure.
Develop subject-matter expertise in battery components and process steps; partner with process, equipment, and R&D engineers to define critical-to-quality characteristics and set specification limits based on process capability and product-performance data.
Work cross-functionally to establish repeatable measurement processes for cells and components, validate them through MSA/Gage R&R, and capture results in the database for use as CTQs and specifications in process-quality tracking.