Senior Staff Software Engineer - Perception & Sensing

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

Zoox is on a mission to reimagine transportation and ground-up build autonomous robotaxis that are safe, reliable, clean, and enjoyable for everyone. We are still in the early stages of deploying our robotaxis on public roads, and it is a great time to join Zoox and have a significant impact in executing this mission. The ML Platform team at Zoox plays a crucial role in enabling innovations in large-scale Foundation models, VLMs, and VLAs to make autonomous driving as seamless as possible. The Opportunity Are you excited to drive our ML Performance Optimization initiatives and make our ML models that enable autonomous driving as fast and efficient as possible? You will get to work with SOTA accelerators, cutting-edge techniques in distributed training, quantization, distillation, and pruning, among other things, working closely with all the Autonomy teams within Zoox - Perception, Prediction, Planner, Simulation, Collision Avoidance, and have the opportunity to significantly push the boundaries of how ML is practiced within Zoox. We build and operate the base layer of ML tools, model development, and serving systems that our applied research teams use for in- and off-vehicle ML use cases. You will work alongside a team of strong software engineers and act as a force multiplier for our internal customers. This team has many growth opportunities as we expand our robotaxi deployments and venture into new ML domains. If you want to learn more about our stack behind autonomous driving, please look here. If you want to learn more about our ML Infrastructure, here is one of our past talks at re:Invent. In this role, you will: Lead the design, implementation, and operation of training performance optimizations (data/tensor/pipeline parallelism, mixed precision, gradient checkpointing, GPU kern

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

Zoox is on a mission to reimagine transportation and ground-up build autonomous robotaxis that are safe, reliable, clean, and enjoyable for everyone. We are still in the early stages of deploying our robotaxis on public roads, and it is a great time to join Zoox and have a significant impact in executing this mission. The ML Platform team at Zoox plays a crucial role in enabling innovations in large-scale Foundation models, VLMs, and VLAs to make autonomous driving as seamless as possible. The Opportunity Are you excited to drive our ML Performance Optimization initiatives and make our ML models that enable autonomous driving as fast and efficient as possible? You will get to work with SOTA accelerators, cutting-edge techniques in distributed training, quantization, distillation, and pruning, among other things, working closely with all the Autonomy teams within Zoox - Perception, Prediction, Planner, Simulation, Collision Avoidance, and have the opportunity to significantly push the boundaries of how ML is practiced within Zoox. We build and operate the base layer of ML tools, model development, and serving systems that our applied research teams use for in- and off-vehicle ML use cases. You will work alongside a team of strong software engineers and act as a force multiplier for our internal customers. This team has many growth opportunities as we expand our robotaxi deployments and venture into new ML domains. If you want to learn more about our stack behind autonomous driving, please look here. If you want to learn more about our ML Infrastructure, here is one of our past talks at re:Invent. In this role, you will: Develop and execute a strategic vision for the ML Performance Optimization team to unlock ML innovation in autonomous driving and rider experience. Lead the design, implementation, and operation of cutting-edge ML Training OR Inference performance optimization techniques to scale our VLM, VLA, and Foundational models and deplo

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