Senior / Staff Software Engineer - Learned Trajectory Machine Learning Engineer

Zoox; Foster City (CA); On-site; USD 242,000 - 349,000; Full time

Benefits offered by this job

Paid time off

Zoox Stock Appreciation Rights

Amazon RSUs

Health insurance

Long-term disability insurance

Life insurance

Job summary

Zoox is seeking a Learned Trajectory Machine Learning Engineer to develop deep learned models that generate driving trajectories for autonomous vehicles. You will collaborate with Planner and Perception teams to advance vehicle behavior, using imitation and reinforcement learning to produce safe and efficient driving plans.

The role involves leveraging large-scale ML infrastructure, crafting metrics, and working across perception, planning, and simulation teams to solve complex urban driving

Qualifications

• Advanced degree in CS or related field
• Experience with training and deploying transformer-based models and reinforcement learning
• Fluency in Python and basic understanding of C++
• Strong mathematical, programming, and algorithm design skills

Responsibilities

• Develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for autonomous vehicles
• Leverage large-scale ML infrastructure to discover new solutions
• Develop metrics and tools to analyze errors and understand improvements of the systems
• Collaborate with Perception, Planning, and Simulation teams to solve the autonomous driving problem in urban environments

Skills

Python

Transformer models

Reinforcement learning

ML pipelines

Mathematics

Education

BS/MS/PhD in Computer Science or related field

Tools

C++ basics

Job description

The Prediction & Behavior ML team is responsible for developing machine learning (ML) algorithms that learn and predict behaviors from data, applying them both on-vehicle to influence driving behavior and off-vehicle to provide ML capabilities to simulation and validation. Given the tight integration of behavior forecasting and motion planning, our team collaborates closely with the Planner team to advance overall vehicle behavior. We also work closely with our Perception, Simulation, and Systems Engineering teams to accelerate our ability to validate our driving performance.

As a Learned Trajectory Machine Learning Engineer you will be responsible for developing deep learned models that produce trajectories for our vehicles to drive. Given the tight integration of behavior prediction and motion planning, you will closely collaborate with the Planner and Perception teams in the advancement of our overall vehicle behavior.

In This Role, You Will

• You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for our autonomous vehicle. You will also work on techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort etc.
• You will leverage 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, and Simulation to solve the overall Autonomous Driving problem in complex urban environments

Qualifications

• BS, MS, or PhD degree in computer science or related field
• Experience with training and deploying transformer-based model architectures and reinforcement learning
• Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
• Fluency in Python with a basic understanding of C++
• Extensive experience with programming, algorithm design, and strong mathematics skills

Bonus Qualifications

• Conference or Journal publications in Machine Learning or Robotics related venues
• Prior experience with Prediction and/or autonomous vehicles or robotics in general

$242,000 - $349,000 a year

Base Salary Range

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.

About Zoox

Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We're looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.

A Final Note

You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.