Machine Learning Engineer - ML Agents and Planning

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.

There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.

Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.

• 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.
• PhD degree in computer science or related field or master's degree and 3+ years 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++
• Top tier publications (NeurIPS, ICML, CVPR)