Staff/Senior Staff Software Engineer - Full Stack

Zoox is on an ambitious journey to develop a full-stack autonomous robotaxi solution, and freeways are the next frontier. We are looking for a Senior Staff or Principal Software Engineer who has played a significant role in removing the driver from an autonomous vehicle operating on freeways, and who can help Zoox navigate that same journey. You will bring deep technical expertise in a relevant autonomy domain — the specific domain is flexible — along with the hard-won judgment that comes from having taken a freeway driving program from supervised operation to driverless. This is a highly visible role with broad influence across the Autonomy organization. In this role, you will: Guide autonomy-wide initiatives toward driverless freeway operation, including setting the technical vision and strategy, building the roadmap, and driving cross-functional execution Identify the highest-risk gaps between our current freeway capabilities and driver-out readiness, and lead the engineering work to close them Develop algorithms that enable freeway driving capabilities, spanning architecture, design, implementation, integration, testing, and verification and validation Communicate technical trade-offs, risk assessments, and readiness recommendations to diverse stakeholders, including senior and executive leadership Mentor engineers across the freeway program and raise the bar for how we design, test, and validate high-speed autonomous driving Qualifications: 10+ years of industry software experience Demonstrated technical leadership of removing the human driver from an autonomous vehicle operating on freeways or highways Deep technical expertise in at least one autonomy domain, such as behavior/motion planning, perception, prediction, simulation, or systems engineering Proven ability to

Zoox is looking for an AWS Cloud Security Engineer to lead the design and implementation of secure, scalable, and software-defined infrastructure in our AWS cloud environment. This role is responsible for establishing best-in-class security practices across AWS, driving automation-first infrastructure security, and partnering with engineering and platform teams to embed security into every layer of our technology stack. You’ll act as a security champion, ensuring that infrastructure designs meet the highest standards of confidentiality, integrity, and availability — while maintaining operational efficiency and scalability through Infrastructure as Code (IaC). In this role, you will: Define and own the multi-year security architecture and roadmap for Zoox's robots and fleet, spanning embedded/firmware, in-vehicle networks, cloud, and supporting infrastructure. Partner with hardware, firmware, autonomy, and infrastructure engineering teams to embed security requirements into system design from the ground up, rather than bolting them on after the fact. Lead threat modeling and architecture reviews for new vehicle platforms and major feature launches, identifying and prioritizing systemic risk across the fleet. Set technical standards and guardrails - secure boot, key management, network segmentation, update/OTA security, and more - and drive their adoption across engineering teams. Represent Product Security in cross-functional and leadership discussions, translating technical risk into business and safety impact for the organization. Qualifications 10+ years of experience in security engineering, including significant time in an architect, staff, or technical lead capacity. Deep expertise in embedded/IoT security, including secure boot, TPM/TEE, cryptographic key management, and hardened firmware/OS design. Demonstrated experience architecting security for complex, physical/cyber-physical sys

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 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 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

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