Machine Learning Engineer - Behavior Models for Road Users
Summary
Develops machine learning models for autonomous driving agents using imitation and reinforcement learning, focusing on planning and prediction to improve safety and performance in simulation environments.
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 or master's degree and 3+ 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
Skills
As published by lever · 5 questions · 2 written answers
Basics
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