Job Title: Lead Principal Machine Learning Engineer

Location: San Jose, CA (Hybrid)

Duration: 12+ Duration

As a deep learning specialist on the metrology team, you will lead efforts to build and develop advanced methods for analyzing image and characterization data collected on our key components at high volume manufacturing scales!

What you will do:

• Lead a team of machine learning engineers to develop state-of-the-art deep learning solutions for analysis of high-resolution, high-velocity image and measurement data, leading to improved understanding of device performance and improved yield.
• Establish standardized workflows for building, deploying, maintaining models
• Serve as a machine learning expert/resource for the broader QuantumScape team
• Collaborate closely with hardware and metrology teams building high-performance, automated inspection technologies, and a multi-disciplinary team of engineers and scientists developing Client materials and products.
• Collaborate with our software infrastructure team to build deep learning pipelines that scale.
• Develop and deploy edge machine learning solutions for high-throughput, automated manufacturing steps.
• Identify and manage external resources (contractors, software providers, consulting firms) to expand capacity or obtain new capabilities.
• Remain up to date on advances in deep learning and machine learning methodologies integrating the most promising techniques into our process.

Knowledge, skills & abilities:

Share a passion for our mission.

Previous experience growing and leading a machine learning, data science, or software team.

Have a track record of building and deploying deep learning solutions in a materials research or manufacturing setting.

Have a track record leading quantitative analysis of image data.

Thrive in a dynamic, technically-challenging environment, and quickly adapt to changes.

Enjoy working as part of a collaborative, multi-disciplinary team to tackle complex challenges.

Minimum requirements:

BS in Computer Science, Materials Science, Physics, Mechanical Engineering, Electrical Engineering, or related field.

At least 8 years of combined professional and academic experience applying deep learning for quantitative data analysis.

2+ years of experience leading or managing teams in this or a related field.

Competence with multiple deep learning frameworks (PyTorch, TensorFlow, Hugging Face).

Competence with Python data analysis stack (Pandas, scikit-learn)

Previous experience with MLOps and leveraging Cloud (e.g. AWS, Google) resources

Fluency in one or more general programming languages, including but not limited to Python, C/C++.

Highly desired:

• Experience running and optimizing training, inference on multiple GPUs
• Domain expertise in one or more areas related to manufacturing or physical sciences.
• Expertise in applying deep learning approaches for complex image segmentation and object detection.
• Experience applying edge-based and continuous data stream processing for near real-time inference.