Senior / Staff Machine Learning Engineer - 3D Sensor Simulation

As a CNC Programmer at Zoox, you'll interface with a broad range of Engineers and Craftspeople to build hardware systems for our Vehicle, Compute, Sensor, and testing fleets. Core values include a positive outlook, good interpersonal skills, and a desire to affect a company genuinely trying to change the world. Familiarity with names like Hermle, DMG Mori, Siemens, Heidenhain, Lang, Haimer, and Robotic Solutions will give you a head start at Zoox. Your experience in a professional and well-equipped CNC shop and positive outlook in a team environment will serve equally well on the path to success. Our team encompasses everything from prototyping to manufacturing operations. Career growth will be at your disposal. We serve an entire company and often volunteer to step outside our disciplines to help all who ask. If this sounds like the position you're looking for, we look forward to meeting you. In this role, you will: Manufacture parts from one-off prototypes to short-run production in a variety of materials Program 5-axis CNC machines using HyperMILL Design and machining of fixtures to hold components during machining operations Setup and teardown of CNC machines Interface with people across the company to acquire the correct set of data needed to make components Assist in ordering materials and tools for the machine shop Develop and maintaining digital tooling and work holding databases Work with engineering teams to provide DFM feedback for machined components Qualifications Extensive experience as a 5-axis CNC mill machinist 10+ years of experience as a CNC programmer using state-of-the-art CAM systems with 3D simulation and collision detection capabilities Familiarity with Heidenhain and Siemens CNC controls Strong CAD skills <p

This role in the Offline Driving Intelligence team is responsible for developing/learning behavior models for road users such as cars, bicycles, and pedestrians. These agents populate Zoox's simulations and must be indistinguishable from real road users, yet fully controllable: promptable into the rare, adversarial, safety-critical behaviors we need to test against. This means the team’s models directly impact how fast Zoox can train, validate and ship its driving stack. Our team collaborates closely with Planner, Simulation and Validation teams to develop and validate our driving performance. As an ML Agents 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... Develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for human-like driving agents. Work on novel techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort and realism. Build generative behavior models (e.g. autoregressive, diffusion) that are conditionable on scenario intent — "cut off the ego vehicle," "jaywalk here" — for targeted stress-testing. Leverage our compute, infrastructure and large corpus of data to push 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. Qualifications PhD degree in computer science or related field and 4+ years of relevant professional experience or master's degree and 7+ years of relevant professional experience

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 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

The Perception team is pioneering the development of a multi-modality foundation model to drive the next generation of autonomous system intelligence. As a Machine Learning and System Optimization Engineer, you will orchestrate and allocate overall system capacity to various core perception models running on-bot, as well as drive large initiatives that allow for more efficient inference by sharing various parts of the perception stack with one another. You will focus on bringing highly efficient, production-ready large-scale models to our on-vehicle stack. We are looking for experts with hands-on experience compressing, accelerating, and deploying complex models, including LLMs, VLMs, or foundation models, for power- and thermal-constrained vehicle SoCs. In addition, you will optimize ML models, write custom CUDA kernels, and build highly concurrent inference code to ensure real-time, deterministic execution on edge devices. In this role, you will: Allocate and distribute system resources (CPU/GPU/interconnect) to various models and inference engines running on the robot. Spearhead cross-cutting initiatives that allow for better compute utilization through sharing/fusing models and better scheduling strategies. Optimize large-scale models (Multi-Modal Sensor Fusion models, LLMs, VLMs) using advanced quantization (PTQ, QAT), pruning, mixed-precision inference frameworks, and parameter-efficient fine-tuning (LoRA, QLoRA). Architect and implement model conversion and compilation pipelines using TensorRT for edge deployment. Write production-level, low-latency, and memory-safe C++ and CUDA code for real-time inference on vehicle systems. Qualifications: Deep experience in system and performance optimization in CPU/GPU systems designed for low latency or high throughput. Deep expertise in working with real-time systems & required

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