SMTS, Process Quality Engineer

QuantumScape Corporation•San Jose, CA

7h•$112,500 - $163,200•Onsite

About The Position

QuantumScape is seeking a Senior Engineer with industry experience in Process Quality. In this role, you will collaborate with process owners and operators to implement control strategies across QuantumScape’s cell and separator manufacturing lines. The ideal candidate thrives in a lab environment, is passionate about optimizing tools and processes, values consistency and documentation, and has a proven track record with statistical analysis. This role is part of the Manufacturing Quality team, which is a diverse group of engineers and data scientists with expertise in materials science, electrochemistry, statistics, and machine learning. The team focuses on data-driven decision-making and continuous improvement of QuantumScape’s solid-state battery technology, working closely with Manufacturing, Reliability, and R&D groups to identify failure modes, engineer critical-to-quality specifications, and implement robust control strategies.

Requirements

Bachelor's degree in Engineering (Chemical, Mechanical, Materials Science, Metallurgy, Industrial, or related discipline) + 5 years of experience in a quality or process engineering role; Master's degree + 3 years; or PhD.
Fundamental understanding of quality and reliability engineering, with deep knowledge of quality tools and methodologies such as SPC, root cause analysis (Fishbone, 5 Whys), 8D problem solving, Six Sigma, Measurement Systems Analysis (Gage R&R).
Hands-on experience with FMEA (DFMEA/PFMEA) and Control Plan development in a manufacturing environment.
Strong foundation in descriptive and inferential statistics — distributions, confidence intervals, and hypothesis testing — applied to manufacturing data; proficiency with JMP or Python (SQL and Minitab a plus).
Excellent communication, presentation, and leadership skills — able to distill key insights from large data sets and influence team members, partners, and stakeholders to drive quality improvements.
Adaptable to shifting priorities in a fast-paced manufacturing environment and self-driven with end-to-end ownership of projects from problem definition to closure.
A first-principles thinker who reasons from underlying physics, process mechanics, and data rather than defaulting to convention, and adjusts quickly as priorities shift.

Nice To Haves

Fundamental understanding of battery electrochemistries and failure mechanisms; familiarity with electrical test and metrics.
Certified Quality Engineer (CQE), Certified Reliability Engineer (CRE), Six Sigma certification, or equivalent.
Experience in cleanroom/dry-room manufacturing.

Responsibilities

Monitor quality of each process by leveraging SPC charts, Cp/Cpk analysis, and defect rate and pareto tracking to flag excursions early and drive continuous improvement.
Review and refine DFMEAs/PFMEAs with process owners and design teams, prioritizing the highest-risk failure modes using RPN or Action Priority.
Develop and maintain Control Plans under revision control — including metrology steps validated through MSA — and ensure shop-floor compliance to documented procedures.
Lead Task Force (TF) and Material Review Board (MRB) on quality excursions — drive containment, root cause investigation, and corrective and preventive actions (CAPA), and author 8D reports for internal/external customers.
Drive the Change Review Board (CRB): screen and classify change requests, prepare risk assessments with change owners, facilitate reviews, track approvals and action items, and maintain auditable change records.
Collaborate with process teams to identify yield improvement and defect reduction opportunities, isolate sources of variation, and drive improvement projects to closure.
Leverage DOE and statistical analysis to formalize and improve sampling protocols for go/no-go testing — defining AQL/RQL targets and selecting appropriate sampling strategies — to reduce downstream escapes.
Use Python or JMP to collect and analyze manufacturing data sets to identify trends, quantify process shifts, and support data-driven decisions on process changes and sampling protocols.