Failure Analysis Engineer

QuantumScape Corporation•San Jose, CA

1d•$88,000 - $105,000•Onsite

About The Position

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. The Failure Analysis (FA) team plays a critical role in advancing next-generation battery technology by uncovering the true root causes behind defects and reliability issues. We are a highly collaborative, cross-functional group with deep expertise across engineering, chemistry, materials science, reliability, and manufacturing. As an onsite R&D and manufacturing organization, we work closely with manufacturing, quality, metrology, and product engineering partners to rapidly learn from failures and translate insights into design and process improvements. The team increasingly relies on data analysis and machine learning to scale defect detection as production ramps. We value curiosity, rigor, teamwork, and hands-on problem-solving, and provide strong mentorship for early-career engineers. We are seeking an Associate Failure Analysis Engineer (AMTS) who is intellectually curious, data-driven, and excited to build a foundation in failure analysis and reliability engineering. This is an early-career role ideal for a recent bachelor's or master's graduate who enjoys turning data into insight and solving problems systematically — no prior industry experience required. You will work alongside experienced FA engineers to analyze failure-mode data, help characterize failure mechanisms in our solid-state battery cells, and translate findings into actionable feedback for the manufacturing and quality teams. Through structured mentorship and real project exposure, you will develop strong technical fundamentals, data and communication skills, and an understanding of how failure analysis informs design and manufacturing decisions at scale.

Requirements

Bachelor's or Master's degree in Chemistry, Chemical Engineering, or Materials Science (or a closely related technical field); thesis not required; completed or expected by start date
No prior industry experience required — this is an early-career / new-graduate role
Strong data-analysis and problem-solving skills, with hands-on experience cleaning, analyzing, and visualizing data. Python preferred (e.g., pandas, NumPy, matplotlib); JMP is acceptable
Familiarity with basic statistics, such as distributions, hypothesis testing, and regression
Working knowledge of material characterization and electrochemistry fundamentals
Ability to communicate technical findings clearly in writing and presentations, including to manufacturing and non-specialist audiences
Comfortable working in and around a laboratory and manufacturing environment; hands-on lab experience is helpful

Nice To Haves

Statistical analysis experience beyond the basics (e.g., DOE, statistical process control / control charts, or reliability statistics)
Machine learning coursework or projects, such as classification, defect detection, regression, or computer vision
Experience building dashboards or data-visualization tools (e.g., Plotly/Dash, Streamlit, Tableau, or Power BI) and querying databases with SQL
Familiarity with version control (Git) and reproducible analysis workflows (e.g., Jupyter notebooks)
Internship, co-op, or research experience in an engineering, chemistry, or manufacturing environment
Interest in energy storage, batteries, or high-reliability systems

Responsibilities

Analyze defect and reliability data across cell batches and test platforms to identify failure-mode patterns and trends
Translate analysis into clear, actionable feedback and recommendations for the manufacturing and quality teams
Apply statistical and data-analysis techniques (Python, JMP) to characterize failure modes and support process improvement
Build and maintain dashboards, data summaries, and visualizations that help teams quickly understand key signals
Support root-cause investigations alongside senior FA engineers, connecting data findings to physical failure mechanisms
Draw on material characterization and electrochemistry fundamentals to interpret battery-related challenges
Document findings in clear, well-structured technical reports and presentations
Spend focused time in the lab as needed to observe characterization, review samples, and ground data analysis in the physical hardware