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Tesla Machine Learning Engineer 3D Computer Vision Self United States, California, Palo Alto 593780375
23.06.2024
What You’ll DoWhat You’ll Bring
- Develop novel formulations and architectures for a wide variety of computer vision tasks
- Perform large-scale distributed training of deep neural networks to build a unified and consistent vector space for autonomous driving tasks (e.g., occupancy, occupancy flow, semantics, geometry, detection, drivable surface)
- Design metrics, tasks, and datasets that aid in perception and autonomy
- Deploy models at scale to millions of Tesla cars in the real world
- Strict adherence to strong software engineering practices to develop novel work quickly and safely
- Strong experience writing production-level Python and software engineering best practices
- Solid mathematical fundamentals including linear algebra, vector calculus, probability theory, and numeric optimization
- An “under the hood” knowledge of deep learning: layer details, loss functions, optimization, etc
- Understanding of modern deep learning techniques (CNNs, transformers, autoregressive models, etc.)
- Domain expertise in at least one of these areas: object detection & tracking, pose estimation, depth estimation, semantic & instance segmentation, video models, differentiable rendering, Neural Radiance Field (NeRF), 3D reconstruction, visual SLAM, structure from motion
- Familiarity with basic computer vision concepts such as intrinsic and extrinsic calibrations, homogeneous coordinates, projection matrices, and epipolar geometry
- Experience with PyTorch, or at least another major deep learning framework such as TensorFlow
- Experience in GPU programming (e.g., CUDA, OpenCL, OpenGL) or GPU-accelerated libraries
- Comfortable working in a shared cluster environment
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