SMTS, Battery Manufacturing Quality Engineer (Data & Imaging) 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, engineer critical-to-quality specifications, and implement robust control strategies across all process areas. What we need: The Manufacturing Quality team is seeking a highly motivated engineer who is energized about improving the quality and reliability of our solid-state Li metal batteries. You will leverage a diverse set of data-driven and computational methodologies to detect critical defect modes on our cells and develop robust specifications to be implemented on the manufacturing line. The ideal candidate has a hard sciences background, battery or semiconductor industry experience, and the ability to communicate complex technical information to a diverse group of stakeholders. If you enjoy problem-solving and thrive in a highly collaborative, fast-paced environment, we'd like to hear from you. What You’ll do: • Develop subject-matter expertise on battery components and collaborate cross-functionally on in-line metrology systems and imaging post-processing techniques. • Develop computer vision detection algorithms and machine learning models, including building comprehensive labeling rulebooks, coordinating labelers, and maintain model performance as processes and chemistries change over time. • Collaborate with R&D engineers through weekly in-person team meetings to generate new metrics or deep learning models that capture defect modes in 2D and 3D images. • Leverage advanced statistical and machine learning techniques to uncover complex patterns in large datasets of battery and component-level electrical test outcomes. • Build predictive models that identify key features in metrology and microscopy images of cells, separators, and cathodes to predict downstream electrical performance, and propose opportunities to tighten specifications to drive improved product reliability. • Collaborate with cross-functional engineers to develop repeatable measurement processes for battery cells and components, where data is uploaded to the database and results serve as CTQs and specifications for process-quality tracking. • Leverage AI agents to query manufacturing data and clean and process it, applying critical judgment to select the best data science methodologies and rapidly apply them to internal projects. Skills You’ll Need: • BS degree in Engineering, Data Science, Statistics, Computer Science, Applied Math, Applied Physics, or equivalent fields. MS or PhD preferred. • 5+ years of experience working on highly complex problems in Materials Science, Physics, Chemistry, Engineering, or equivalent fields, such as the battery or semiconductor industries. • Strong programming skills, especially in Python, including 2+ years of experience with data science and machine learning libraries such as Pandas, NumPy, SciPy, scikit-learn, TensorFlow, and PyTorch. • Experience with computer vision and image processing techniques (e.g., image segmentation, object/defect detection, OpenCV) applied to 2D and/or 3D imaging data. • Industry experience with statistical analysis for manufacturing processes, such as regression (e.g., linear, logistic), t-tests, comparison of different test groups, and survival analysis. • Experience developing machine learning models such as tree-based models (e.g., decision trees, random forest, XGBoost) or deep learning models (e.g., neural networks, autoencoders) to predict binary or continuous outcomes using large, complex datasets. • Ability to communicate findings to colleagues from various disciplines through clear and concise reports, presentations, and data visualizations. Nice to have: • Fundamental understanding of battery electrochemistries and failure mechanisms. Familiarity of electrical test and metrics. • Proficiency with SQL to query data from database and data warehouse storage (e.g., GCP's BigQuery). • Experience deploying and monitoring machine learning models in production, or familiarity with MLOps practices for maintaining model performance over time. • Proficiency with AI-assisted tooling for querying, cleaning, and processing data, to accelerate data analysis and visualization, model development, etc. • Proficiency with JMP, Microsoft Office, and VSCode. Physical requirements: • Occasional work is performed in a dry room or lab area, requiring gowning in a full bunny suit • Sitting or standing for extended periods of time ONSITE: This position is required to work onsite 4-5 days per week to meet the minimum essential duties and requirements of this position.  As an on-site R&D and manufacturing operations organization, in-person face to face interaction is essential to building authentic relationships, trust, teamwork, and collaboration.  Compensation & Benefits: Expected salary range for this role is from $112,500 to $163,200 and a final salary will be determined by the candidate's experience, educational background and internal equity. QuantumScape also offers an annual bonus and a generous RSU/Equity package as part of its compensation plan. In addition, we do offer a tremendous benefits plan including employee paid health care, Employee Stock Purchase Plan (ESPP), and other benefits.  We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive benefits and privileges of employment. Please contact us to request an accommodation.

Requirements

Must-haves

Develop subject-matter expertise on battery components and collaborate cross-functionally on in-line metrology systems and imaging post-processing techniques.
Collaborate with R&D engineers through weekly in-person team meetings to generate new metrics or deep learning models that capture defect modes in 2D and 3D images.
Leverage advanced statistical and machine learning techniques to uncover complex patterns in large datasets of battery and component-level electrical test outcomes.
Leverage AI agents to query manufacturing data and clean and process it, applying critical judgment to select the best data science methodologies and rapidly apply them to internal projects.
5+ years of experience working on highly complex problems in Materials Science, Physics, Chemistry, Engineering, or equivalent fields, such as the battery or semiconductor industries.
Strong programming skills, especially in Python, including 2+ years of experience with data science and machine learning libraries such as Pandas, NumPy, SciPy, scikit-learn, TensorFlow, and PyTorch.
Experience with computer vision and image processing techniques (e.g., image segmentation, object/defect detection, OpenCV) applied to 2D and/or 3D imaging data.
Industry experience with statistical analysis for manufacturing processes, such as regression (e.g., linear, logistic), t-tests, comparison of different test groups, and survival analysis.
Ability to communicate findings to colleagues from various disciplines through clear and concise reports, presentations, and data visualizations.
Occasional work is performed in a dry room or lab area, requiring gowning in a full bunny suit
Sitting or standing for extended periods of time

Nice-to-haves

BS degree in Engineering, Data Science, Statistics, Computer Science, Applied Math, Applied Physics, or equivalent fields. MS or PhD preferred.
Fundamental understanding of battery electrochemistries and failure mechanisms. Familiarity of electrical test and metrics.
Proficiency with SQL to query data from database and data warehouse storage (e.g., GCP's BigQuery).
Experience deploying and monitoring machine learning models in production, or familiarity with MLOps practices for maintaining model performance over time.
Proficiency with AI-assisted tooling for querying, cleaning, and processing data, to accelerate data analysis and visualization, model development, etc.
Proficiency with JMP, Microsoft Office, and VSCode.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaipythonsqlgcp

Platform: successfactors
Effort: medium

Benefits

Benefits detected in the listing.

Vision