Senior Machine Learning Engineer - ML Agents and Planning

Zoox•Foster City, CA

4d•$242,000 - $290,000

About The Position

The Offline Driving Intelligence team is responsible for developing Foundation Models for ML Agents and planning, applying them off-vehicle to provide generalization capabilities to simulation and validation. Our team collaborates closely with the Planner, Simulation and Validation teams to develop and validate our driving performance. As an ML Agents and Planning Machine Learning Engineer you will work on the bleeding edge of the industry, developing novel machine learning pipelines and models to predict the behavior of other agents in the world and planning the best course of action for the ego vehicle. In this role, you will... You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for human-like agents. You will work on novel techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort and realism. You will contribute to our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field You will develop metrics and tools to analyze errors and understand improvements of our systems You will collaborate with engineers on Perception, Planning,Simulation, and Validation to solve the overall Autonomous Driving problem.

Requirements

PhD degree in computer science or related field +1y of professional experience (top tier publications can remove the need for the year of experience) or, MSc +5y of professional experience in a relevant field.
Experience in Planning and / or Prediction using Reinforcement Learning techniques
Experience with training and deploying transformer-based model architectures
Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
Fluency in Python with a basic understanding of C++

Nice To Haves

Top tier publications (NeurIPS, ICML, CVPR)

Responsibilities

Develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for human-like agents.
Work on novel techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort and realism.
Contribute to our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field
Develop metrics and tools to analyze errors and understand improvements of our systems
Collaborate with engineers on Perception, Planning,Simulation, and Validation to solve the overall Autonomous Driving problem.