Deep Learning Specialist
- Silicon Valley, CA, United States
Come join us on our mission as we revolutionize energy storage to enable a sustainable future.
We are a well-funded start-up located in San Jose, California with a new energy storage technology that will allow electric vehicles to outperform internal combustion cars. We have enabled a step-change in performance with a different chemistry and cell architecture that also benefits safety.
Do you want to make a major contribution to this critical part of the future energy economy? 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!
- Develop state-of-the-art deep learning solutions for analysis of high resolution, high velocity image data, leading to improved understanding of device performance and improved yield.
- Collaborate closely with hardware and metrology teams building high-performance, automated inspection technologies, and a multi-disciplinary team of engineers and scientists developing novel materials and products.
- Build deep learning pipelines that scale.
- Develop and deploy edge machine learning solutions for high-throughput, automated manufacturing steps.
- Develop and deploy machine learning and analytical solutions for data collected with cutting edge materials characterization equipment.
- Remain up to date on advances in deep learning and machine learning methodologies and bring the most promising methods into use.
Knowledge, skills & abilities:
- Share a passion for our mission.
- Have a track record of building and deploying deep learning solutions in a materials research or manufacturing setting.
- 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.
- BS in Computer Science, Materials Science, Physics, Mechanical Engineering, Electrical Engineering, or related field.
- At least 3 years of combined professional and academic experience applying deep learning to quantitative image analysis.
- Competence with one or more deep learning frameworks (PyTorch, TensorFlow, Keras, fastai).
- Fluency in one or more general programming languages, including but not limited to Python, C/C++.
- 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.
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.
*QuantumScape does not accept unsolicited resumes from individual recruiters or third party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers. https://jobs.lever.co/quantumscape/58e84f07-9bce-43c5-92da-ad52df4c67da
We are redefining the frontier of battery technology by introducing a new battery chemistry that will enable lower cost and higher energy density batteries. We will enable a mass market electric vehicle that is affortable, long range, fast charging, and safer and longer lasting than today's EVs.
We value integrity, customer service, professional excellence, and transparency. We work hard and expect high quality work from each other; we also play together (examples include Friday basketball, weekend camping trips, annual 5k challenge, summer BBQ and holiday parties).
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