In this role, you will...
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Develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for human-like driving agents.
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Work on novel techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort and realism.
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Build generative behavior models (e.g. autoregressive, diffusion) that are conditionable on scenario intent — "cut off the ego vehicle," "jaywalk here" — for targeted stress-testing.
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Leverage our compute, infrastructure and large corpus of data to push boundaries of the field.
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Develop metrics and tools to analyze errors and understand improvements of our systems.
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Collaborate with engineers on Perception, Planning, Simulation, and Validation to solve the overall Autonomous Driving problem.
Qualifications
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PhD degree in computer science or related field and 4+ years of relevant professional experience or master's degree and 7+ years of relevant professional experience
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Experience in one of the following: Planning, Prediction, Reinforcement Learning, Imitation Learning, generative modeling (diffusion, autoregressive models)
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Experience with training and deploying transformer-based model architectures
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Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
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Fluency in Python ML frameworks and a basic understanding of C++
Bonus Qualifications
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Top tier publications (NeurIPS, ICML, CVPR)
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Experience with JAX
