What to Expect

‍Our team productionalizes ML models - we train and deploy large neural networks for efficient inference on compute-constrained edge devices (CPU / GPU / AI ASIC). The nature of this role is multi-disciplinary - you will work at the intersection of machine learning and systems by building the ML frameworks and infrastructure that enable the seamless training, deployment, and inference of all neural networks that run on Autopilot and Optimus.

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‍What You’ll Do

• Build robust AI frameworks to lower neural networks to edge devices
• Build robust AI infrastructure to train and fine-tune networks for Autopilot and Optimus on large GPU clusters
• Deploy state-of-the-art neural networks on heterogenous compute, including Tesla’s in-house AI ASIC, with an aim to maximize network performance while minimizing latency
• Collaborate with AI scientists and compiler engineers to effectively compress large models to run in low precision
• Design and implement custom GPU kernels (CUDA / OpenCL) for efficient training and post-processing of network outputs

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What You’ll Bring

• Proficiency with Python and C++, including modern C++ (14/17/20)
• Proficiency with PyTorch or another machine learning framework
• Proficiency with training and deploying neural networks for real-world AI
• Proficiency with computer systems and computer architecture
• Experience with CUDA

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