Zoox

<div>We are seeking an experienced and analytical Contingent Workforce Program (CWP) Manager to oversee our high-volume, end-to-end CW operations. In this role, you will lead a dedicated team of Program Specialists, manage supplier performance, and drive operational excellence across our fast-paced program ecosystem.</div>
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<div>You will be instrumental in optimizing our high-volume talent acquisition funnel, analyzing metrics from requisition intake to worker offboarding, and translating program data into actionable insights for leadership. If you thrive on data-driven strategy, team leadership, and building strong supplier partnerships in a dynamic environment, this role is for you.</div>
<h2>In this role, you will:</h2><ul><li>Lead, mentor, and develop a high-performing team of Contingent Workforce Specialists.</li>
<li>Oversee day-to-day operations to ensure requisitions are distributed, managed, and filled in alignment with SLAs.</li>
<li>Drive continuous process improvement across program operations to streamline workflows, eliminate bottlenecks, and increase efficiency.</li>
<li>Serve as the primary escalation point for complex operational challenges, client queries, and supplier issues.</li>
<li>Conduct end-to-end requisition funnel analysis, tracking key performance indicators (KPIs) such as submission-to-hire ratios, interview-to-hire ratios, time-to-fill, and fill rates.</li>
<li>Perform in-depth attrition analysis to identify root causes of early worker roll-offs and surface labor trends to improve retention.</li>
<li>Leverage analytical insights to identify process improvement opportunities within the talent acquisition lifecycle.</li>
<li>Transform raw workforce data into meaningful market intelligence and executive-ready reporting.</li>
<li>Manage relationships with partner staffing agencies and vendors to drive delivery quality and fill capability.</li>
<li>Train and onboard new suppliers and conduct monthly supplier meetings.</li>
<li>Develop, monitor, and maintain Supplier Scorecards to evaluate vendor effectiveness and hold suppliers accountable to SLAs.</li>
<li>Organize and facilitate periodic supplier performance reviews and tiering strategies.</li>
<li>Act as a trusted advisor to hiring managers and internal stakeholders regarding labor market dynamics, pay rates, and contingent workforce strategy.</li>
<li>Monitor program compliance, legal guidelines, and vendor contract adherence</li></ul>
<h2>Qualifications:</h2><ul><li>6+ years of experience in Contingent Workforce Management, Managed Service Provider (MSP) operations, with at least 2+ years managing direct reports.</li>
<li>Hands-on experience with Vendor Management Systems (e.g., Fieldglass or Beeline).</li>
<li>Strong analytical acumen with proven experience deriving insights from metrics  (funnel performance, attrition trends, rate benchmarks).</li>
<li> Demonstrated success managing staffing supplier networks, SLA tracking, and scorecard creation.</li>
<li>Exceptional verbal, written, and presentation skills suited for executive stakeholders and QBR deliveries.</li>

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<h2>Bonus Qualifications:</h2><ul><li>Certified Contingent Workforce Professional.</li>
<li>Background in market rate benchmarking and contingent labor compliance (co-employment, tenure management).</li></ul>

Program Manager, Contingent Workforce

<div>The Core Data Science team builds the products and data assets that let teams across Zoox measure and improve the autonomy and operational performance of our fleet. In this role, you'll focus on fleet simulation — the environment our data teams use to develop, test, and evaluate the algorithms that orchestrate our autonomous fleet (dispatch, routing, and vehicle assignment) and to understand how those algorithms perform before they reach the road.</div>
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<div>You’ll piece together the constituent components into a coherent, easy-to-use simulation environment, filling in the gaps, and building generalizable mechanisms so data scientists can measure their algorithm's impact on fleet efficiency. You'll join a diverse, experienced team with rapidly growing scope while working with one of the industry's most valuable autonomous vehicle datasets.</div>
<h2>In this role, you will:</h2><ul><li>Turn existing fleet-simulation components into a coherent, self-service environment that data scientists and ML engineers can run without deep software expertise.</li>
<li>Fill gaps and build the connective tissue between simulation, data, and fleet-orchestration algorithms.</li>
<li>Build generalizable mechanisms that let teams measure how their orchestration algorithms affect fleet efficiency.</li>
<li>Collaborate with data science, ML, and fleet-orchestration teams to shape and prioritize the simulation roadmap.</li>
<li>Establish best practices and processes around simulation, evaluation, and data-driven decision-making.</li></ul>
<h2>Qualifications</h2><ul><li>Software development experience in a production environment, with fluency in Python; or demonstrated experience learning new programming languages.</li>
<li>Experience building tools, services, or frameworks that make complex systems usable by non-specialist users.</li>
<li>Familiarity with any version control system, with preference for Git.</li>
<li>Experience designing metrics and building mechanisms that deliver robust, actionable insights.</li>
<li>Good communication and collaboration skills.</li></ul>
<h2>Bonus Qualifications</h2><ul><li>Basic familiarity with C++ and/or Kotlin.</li>
<li>Experience with microservices and/or distributed systems.</li>
<li>Experience with simulation and/or fleet orchestration (dispatch, routing, vehicle assignment).</li></ul>

Software Engineer, Fleet Simulation (Core Data Science)

<div><span>The Offline Driving Intelligence team is responsible for developing Foundation Models for ML Agents and planning, applying them off-vehicle to provide generalization capabilities to simulation and validation. Our team collaborates closely with the Planner, Simulation and Validation teams to develop and validate our driving performance. As an ML Agents and Planning 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.</span></div>
<h2>In this role, you will...</h2><ul><li>You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for human-like agents.</li>
<li>You will work on novel techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort and realism.</li>
<li>You will contribute to our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field</li>
<li>You will develop metrics and tools to analyze errors and understand improvements of our systems</li>
<li>You will collaborate with engineers on Perception, Planning,Simulation, and Validation to solve the overall Autonomous Driving problem.</li></ul>
<h2>Qualifications</h2><ul><li>PhD degree in computer science or related field or master's degree and 3+ years of professional experience in a relevant field.</li>
<li>Experience in Planning and / or Prediction using Reinforcement Learning techniques </li>
<li>Experience with training and deploying transformer-based model architectures</li>
<li>Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines</li>
<li>Fluency in Python with a basic understanding of C++</li></ul>
<h2>Bonus Qualifications</h2><ul><li>Top tier publications (NeurIPS, ICML, CVPR)</li></ul>

Machine Learning Engineer - ML Agents and Planning

<div>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 ML and CV to make autonomous driving as seamless as possible. </div>
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<div><strong>The Opportunity</strong></div>
<div>Would you like to be part of the ML Training platform team that enables autonomous driving, driving scenario understanding, learned planning and trajectories, large scale driving simulations and several other ML use cases at Zoox? You will get to work across all ML teams within Zoox -  Foundation Models, Perception, Prediction, Planner, Simulation, Data Science, Collision Avoidance, etc. and have the opportunity to significantly push the boundaries of how ML is practiced within Zoox.</div>
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<div>The ML Training platform team builds and operates the core part of the ML platform that powers model training at scale. We are responsible for developing and operating ML tools, deep learning frameworks, and distributed model training infrastructure to support foundational models and reinforcement learning. This team also owns the model repository and model lifecycle management tools used by our applied research teams for in- and off-vehicle ML use cases. You will play a crucial role in reducing the time it takes from ideation to productionization of cutting-edge AI innovation. This team has a lot of growth opportunities as we expand our robotaxi deployments and venture into new ML domains.</div>
<h2>In this role, you will:</h2><ul><li>Build the Zoox Training framework leveraged by all ML teams within Zoox. This framework needs to be highly scalable, reliable, and efficient.</li>
<li>Design, implement, and operate a robust and efficient ML platform to enable the training, validation, serving, and monitoring of ML models.</li>
<li>Collaborate closely with cross-functional teams, including ML researchers, software engineers, and data engineers, to define requirements and align on architectural decisions.</li></ul>
<h2>Qualifications</h2><ul><li>2+ years of ML infrastructure experience.</li>
<li>Experience with training frameworks like PyTorch, DeepSpeed, JAX, Ray, etc.</li>
<li>Experience working with cloud providers like AWS.</li></ul>
<h2>Bonus Qualifications</h2><ul><li>Experience with building large-scale, cost-efficient distributed model training and ML compute infrastructure.</li>
<li>Experience with building model lifecycle management tools and experimentation</li></ul>

Software Engineer, ML Platform (ML Training)

<div>The Perception team at Zoox is responsible for the robot’s understanding of the world, fusing data from Lidar, Radar, and Cameras to create a unified representation of the environment. In this role, you will contribute to the development of our next-generation 3D occupancy and segmentation networks. You will architect and optimize high-performance deep learning models that generate dense, temporally consistent voxel representations of the driving environment. This work is critical for enabling our vehicle to navigate complex urban scenarios, handle rare obstacles, and drive safely in tight spaces by providing precise geometry and motion estimates to downstream planners.</div>
<h2>In this role, you will...</h2><div>

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<p>Design and implement state-of-the-art multi-modal sensor fusion architectures (Lidar, Camera, Radar) to predict 3D occupancy, semantic segmentation, and flow .</p>
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<p>Develop "vision-first" fusion strategies to enhance geometric understanding and reduce dependency on sparse sensor modalities .</p>
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<p>Engineer temporal processing modules to improve the stability and consistency of predictions over time.</p>
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<p>Optimize model architectures for real-time on-vehicle inference, balancing high-fidelity range extension with strict latency constraints .</p>
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<p>Collaborate with downstream consumers (Tracking, Prediction, Planner) to refine geometric outputs, such as contours and free-space estimations, for complex maneuvering.</p>
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<h2>Qualifications</h2><div>

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<p>MS or PhD in Computer Science, Robotics, Machine Learning, or related field with 6+ years of industry experience.</p>
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<p>Deep expertise in 3D Computer Vision and Deep Learning, specifically with voxel-based or BEV (Bird's Eye View) architectures.</p>
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<p>Strong proficiency in Python and deep learning frameworks (PyTorch) for model training and design as well as some experience in C++ for model integration.</p>
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<p>Experience with multi-sensor fusion (Lidar, Camera, Radar) and handling temporal data sequences.</p>
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<p>Experience with occupancy networks, implicit representations (NeRF/Gaussian Splats), or scene flow estimation.</p>
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<h2>Bonus Qualifications</h2><div>

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<p>Experience optimizing models for TensorRT/CUDA to achieve low-latency inference.</p>
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<p>Familiarity with sparse convolutions or query-based architectures for efficient 3D processing.</p>
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<p>Experience with Vision Language Model, or multi-modal 3D foundation model, or World Model, or VLA.</p>
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Zoox

Open jobs at Zoox

Program Manager, Contingent Workforce

Software Engineer, Fleet Simulation (Core Data Science)

Machine Learning Engineer - ML Agents and Planning

Software Engineer, ML Platform (ML Training)

Senior Machine Learning Engineer - Perception 3D Segmentation

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