Quality Inspection Engineer – Vehicle Engineering

The Vision Automation team is part of the Vehicle Engineering organization. We own the centralized technical aspects of our multi-continent Gigafactory network and product line, including Tesla’s most important programs, such as the four S3XY models, Cybertruck, cell production, Robotaxis, Optimus, and new factories. The quality inspection team plays a role in making the factories every bit as revolutionary as the products themselves.

As a Quality Inspection Engineer on the Vision Automation team, you will contribute to Tesla’s in-house efforts to purpose-build automated inspection systems. The ideal candidate will start learning our current technologies used for model training, deployment, and inspections. Once onboarding is complete, they will lean on their deep understanding of signal processing (especially image processing) and deep learning to improve our systems, including but not limited to 2D/3D perception, model building, model deployment, and rapid or agile prototyping.

• Rapidly prototype machine learning and computer vision systems (including algorithms, code, and devops) that add value or reduce waste in Tesla industrial facilities, then integrate your work with existing vision automation software and operations.
• Learn how Tesla’s automated inspection systems currently at work, then suggest and implement improvements

• MSc or evidence of exceptional ability in Computer Science or related field
• Experience in end-to-end machine learning solution design
• Willingness to work full time on a production floor in Fremont, CA
• Experience in working with 3D cameras
• Proficiency in containerizing through Docker
• Nice to have: Theoretical understanding of major concepts in artificial intelligence, including convolutional neural networks, but also game theory, Markov Decision Processes, Reinforcement Learning, Nonlinear Optimization, and Linear Programming
• Experience with cluster and cloud computing
• Proficiency in OpenCV, Open3D or PCL, Pytorch, and Tensorflow libraries
• Basic understanding of a low-level, strongly typed language like C++ or Go
• Proficiency with a high-level, interpreted language, like Python or Ruby
• Deep understanding of computer vision fundamentals including but not limited to perception, deep learning, object detection, instance and semantic segmentation, and sensor fusion in 2D and 3D