Senior / Staff Machine Learning Engineer - Behavior Models for Road Users
At a glance
Senior Machine Learning Engineering role at Zoox. Foster City · full-time · $254,000–$366,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build deep learning models with imitation and reinforcement learning to create human-like plans for simulated road users
- Generate controllable driving behaviors tied to scenario goals, including cut-ins and pedestrian jaywalking, for targeted stress tests
- Assess driving plans for safety, progress, comfort and realism, then develop metrics and tools to investigate system errors
- Use large datasets and compute resources to advance behavior models, and work with perception, planning, simulation and validation engineers
What you bring
- A PhD in computer science or a related field plus at least four years of relevant work, or a master’s degree plus at least seven years
- Professional experience in planning, prediction, reinforcement learning, imitation learning or generative modeling
- Hands-on experience training and deploying transformer models and building production ML pipelines for data, training and metrics
- Fluency with Python machine-learning frameworks and basic C++ knowledge
Who this fits
This senior or staff-level role suits an experienced ML engineer focused on learned behavior models for simulated road users. It is based in Foster City and listed as hybrid.
From the employer
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