Senior / Staff Machine Learning Engineer - Behavior Models for Road Users

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 Pyt

Zoox is looking for an experienced software engineer to work on large-scale simulation pipelines used to validate the behavior of the Zoox self-driving vehicle. These are data and GPU intensive workloads built on Ray.io and Kubernetes. Given the massive scale and criticality of these pipelines, ensuring their reliability and efficiency has a significant impact on the company's ability to safely and quickly iterate on autonomy development. We are a small, scrappy team within the larger Autonomy organization. Although this role primarily involves off-vehicle pipelines, you will work closely with engineers developing the on-vehicle algorithms and models in our autonomy stack. We stay close to the end users - autonomy engineers - and think about the end to end use case for these validation pipelines. This is a hands-on role with a high degree of independence and ownership. You will be expected to contribute towards the framework’s architecture, reliability, efficiency, and grow its capabilities to support new use cases. You should have a track record of keeping production systems running with high availability. Experience with robotics or autonomous systems is not required but an understanding of the robotic data lifecycle is preferred. In this role, you will: Improve the cost efficiency, reliability, and performance of our validation and simulation pipelines Create production-grade APIs, SDKs, and tools to enable a varied set of validations of autonomous behaviors Improve the ML training pipelines supporting the autonomous behavior org Qualifications Bachelor’s degree in Computer Science or related field and 8+ years of industry experience Experience optimizing large-scale distributed systems for cost and efficiency Experience with AWS or similar providers Proficiency with Python and familiarity with C++ Bonus Qu

The Autonomy Behaviors team is responsible for producing our vehicle’s driving trajectory. As a Principal ML Engineer, you will lead the technical development of machine learning algorithms for our next-generation ML Planner. Your impact will range from delivering innovations for immediate milestones to pioneering exploratory projects. You will collaborate closely with teams specializing in Foundation Models, Perception, Simulation, and Safety Validation, in influencing our autonomy stack. Your role will look at problems in a way that crosses team boundaries to prototype new approaches that influence the long term technical direction of multiple organizations within the company. In this role, you will: Set the technical direction of ML development for the future of our Planner Push the frontiers of imitation learning, reinforcement learning, and model scaling to develop new ML models for our onboard Planner Identify and drive force-multipliers for ML development, including metrics, data, and training frameworks Develop new methods to leverage Zoox’s Foundation Models into the ML Planner Provide technical mentorship to the broader group of ML developers at Zoox Collaborate with engineers across Autonomy Software, Simulation, System Design and Mission Assurance, and other departments to solve the overall Autonomous Driving problem in complex urban environments Qualifications BS, MS, or PhD degree in computer science or related field Proven track record of technical leadership in large-scale AI organizations Significant experience with training and deploying Deep Learning models to production Fluency in C++ or Fluency in Python with a basic understanding of C++ Extensive experience with programming and algorithm design-Strong mathematics skills, 10+ years of experience Bonus Qualifications Conference or Journal publications in Machine Learning or Robotics related v

The Behavior Capabilities opportunity falls within the Fail Operational Planning team at Zoox. This team works to ensure that the robot continues to operate effectively, even in the presence of minor compartmentalized failures or degraded capabilities. The focus of the team includes developing behavioral and motion planning algorithms to improve trip progress, vehicle uptime metrics, and correspondingly a better customer experience. This is a cutting-edge problem which requires teamwork and collaboration across a diverse group of experts including Planner, Perception, Prediction, Hardware, and more. On this team, you will contribute to the next generation of algorithmic advancements to expand our robot’s ability to drive in some of the most complex scenarios. We are looking for an engineer who is excited to understand the interdependencies within the driving stack and takes ownership of their projects and has a holistic product focused approach towards development. In this role, you will: Create principled algorithmic improvements to specific areas of driving behavior and implement those changes in our code base. Collaborate cross-functionally to develop strategies and intelligent behaviors to mitigate the performance degradation under various failure modes of the system. Architect and implement scalable solutions ensuring seamless integration with the rest of the driving stack. Partner with validation teams to define and execute tests for developed algorithms. Qualifications: Master's degree in Computer Science or related field and 9+ years of experience Fluency in C++ Extensive experience with programming and algorithm design Understanding of vehicle kinematics Bonus Qualifications PhD in Computer Science, Electrical Engineering, Mechanical Engineering, or related field Experience shipping products to market (commercial robotics, drones, consumer devices etc.) Demon

On the Motion Planning team at Zoox, you’ll be dedicated to improving the driving behaviors of the robot on public roads. The Planner's job is to figure out where the robot is going and how to get there safely in situations as complex as those found in Downtown San Francisco or on the Las Vegas Strip. This is a cutting-edge problem, which requires teamwork and collaboration across a broad set of experts spanning Planner, Perception, Prediction, Operations, and more. Together, you will create the next generation of algorithmic improvements to expand our robot’s ability to drive in some of the most complex situations found on public roads. Safety is foundational at Zoox, so you will take a measured approach – develop a principled modification to the Planner, test in simulation and on closed courses, and team up with our safety drivers for on-road testing. In this role, you will: Create principled algorithmic improvements to specific areas of driving behavior and implement those changes in our code base. Use our extensive backend tooling to test your changes across many different driving situations at the press of a button. Lead on-vehicle drive reviews to generate actionable feedback on driving performance. Use this experience to help the team learn and make improvements. Attend conferences and survey literature to stay informed on the most recent developments in the fields of Robotics and Motion Planning. Write and file patents for novel technologies you develop. Qualifications Master's degree in Computer Science or a related field and 8+ years of experience Fluency in C++ Understanding of configuration spaces and a variety of planning techniques (A*, RRTs, PRMs, etc.) Demonstrated ability to create real-time motion planning algorithms Bonus Qualifications Significant contributions to geometric- and/or sampling-based planning algorithms Experience with autonomou

As a Technical Lead on Zoox's internal agentic platform, you will own end-to-end integration of a tool-centric agent runtime with external and internal agent clients, line of business backends, scheduled and interactive agent workloads, and cloud operations (Kubernetes, observability, secrets management). You will translate architecture and security intent into stable tool contracts, testable integration milestones, and repeatable releases, and you will be the primary engineer when issues sit at the boundary between product surfaces, platform services, and upstream systems. This role is platform and service integration for an agentic framework that mediates tools (discovery, invocation, policy, and telemetry). Success is measured by reliable tool call paths over HTTP, correct authentication and scoping, environment parity from dev through production, and clear ownership across partner engineering, security, and infrastructure teams. In this role, you will: Own and lead integration strategy and execution across hosted services, shared libraries, standardized tool protocols (e.g., JSON-RPC over HTTP), REST APIs, and diverse callers (vendor agents, consoles, automation workers). Drive cross-functional delivery with backend API teams, partner agent owners, security, SRE/platform, and program management, ensuring formal sign-off where required. Debug and root-cause complex failures using structured logs, distributed traces, metrics, and gateway/session-token behavior, documenting durable mitigations and runbooks. Define and review all integration artifacts (contracts, allowlists, registry config, environment matrices, and interface assumptions) that upstream and downstream teams implement against. Contribute to release gating by ensuring container image promotion, configuration/secrets posture, smoke path execution for high-traffic flows, and disciplined branching/tagging for production inte