ML Engineer, Driving Agents & Planning

Zoox; Northern, Foster City (KY, CA); Hybrid; USD 199,000 - 270,000; Full time

Benefits offered by this job

Stock Appreciation Rights

Amazon RSUs

Health insurance

Paid time off

Job summary

Zoox is seeking an ML Agents and Planning Machine Learning Engineer to develop deep learning models using imitation and reinforcement learning to generate driving plans for human-like agents. You will estimate plan quality across safety, progress, comfort and realism while contributing to scalable ML infrastructure.

You will collaborate with Perception, Planning, Simulation and Validation teams to tackle autonomous driving challenges and push the envelope of the field.

Qualifications

• PhD degree in computer science or related field or master's degree and 3+ years of professional experience.
• 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++.

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 safety, progress, comfort and realism.
• Contribute to large-scale ML 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 Autonomous Driving problems.

Skills

Reinforcement Learning

Transformer models

Python

Planning

Imitation learning

ML pipelines

Education

PhD in Computer Science

Master's degree in related field

Tools

Python (basic)

C++ (basic)

Job description

Zoox is seeking an ML Agents and Planning Machine Learning Engineer to develop deep learning models using imitation and reinforcement learning to generate driving plans for human-like agents. You will estimate plan quality across safety, progress, comfort and realism while contributing to scalable ML infrastructure.

You will collaborate with Perception, Planning, Simulation and Validation teams to tackle autonomous driving challenges and push the envelope of the field.