Fleet Associate Jobs

Zoox builds and operates a fleet of purpose-built robotaxis, and every vehicle must prove its build integrity before it can carry riders. The HWSI team builds the platform that authors, schedules, and executes Build Integrity Tests (BIT) across the manufacturing line and the fleet, and turns the results into the data that engineers and operators rely on every day. In this role, you will: Own the architecture and core of our test-orchestration platform - the distributed task scheduling, execution, and result-collection engine that runs tests across manufacturing and the fleet - and you'll personally build its most complex, reliability-critical components. Lead a small engineering team of two engineers plus 1–3 contractors: setting technical direction, reviewing code, and growing the team's impact. Make the platform resilient at scale - design for safe mid-run cancellation, no result loss under failure, and predictable behavior across hundreds of test runs as the line moves toward high production rate. Build clean APIs and frameworks that let other teams plug their own test suites into the platform without forking it, multiplying the team's reach ac

We are seeking an experienced Fleet Support Inventory Planning Manager to lead a team of inventory and transportation planners responsible for ensuring parts availability that supports our service operations, field maintenance, and fleet uptime. This role is critical to maintaining high service reliability, minimizing vehicle downtime, and ensuring the right parts are positioned in the right locations based on usage patterns, service forecasts, and operational priorities. The ideal candidate brings deep experience in service parts planning, after-sales support, and spare parts lifecycle management. This individual will work cross-functionally with Service Operations, Field Maintenance, Procurement, Engineering, Logistics, and Finance to ensure service parts are accurately forecasted, sourced, stocked, and replenished across the network. In this role, you will: Lead and develop a team of service parts and transportation planners. Oversee inventory planning and replenishment for all service and maintenance parts. Build and execute service parts strategies balancing availability, cost, and working capital. Ensure material readiness to support scheduled and unscheduled fleet service with zero downtime. Manage part lifecycle transitions including new service parts, supersessions, revisions, and end-of-life. Maintain accurate SAP (S/4HANA) planning data, including MRP parameters, lead times, and safety stock. Analyze consumption trends, failure rates, and field demand to forecast and right-size inventory levels. Collaborate with Procurement and suppliers to ensure reliable lead times and supply continuity. Coordinate transportation and logistics to ensure timely delivery of critical service materials. Monitor and report on KPIs such as fill rate, backorders, days of supply, and fleet uptime impact.

About the Team ChatGPT relies on a large and growing GPU fleet to serve inference workloads reliably and efficiently. We develop the systems and tools that make it possible to introduce new models, manage production deployments, respond to operational issues, and use infrastructure effectively at scale. Our work spans distributed systems, platform engineering, infrastructure automation, and developer experience. We partner closely with research, infrastructure, and product teams to make model deployment more reliable, more efficient, and easier to manage. About the Role We are looking for a software engineer with experience building or operating large-scale production systems. You will design and develop systems that support the model lifecycle in production, including deployment orchestration, configuration management, operational automation, reliability, and capacity management. You will help transform complex operational processes into scalable platform capabilities that enable teams across OpenAI to deploy and manage models with greater confidence and less manual effort. This role is a good fit for engineers who enjoy solving complex operational problems and building software that makes production infrastructure easier to run at scale. In This Role, You Will Build and evolve the platform used to deploy, configure, and manage models across ChatGPT. Develop systems for deployment orchestration, model rollouts, operational visibility, and production readiness. Create abstractions and tooling that simplify complex infrastructure and improve the developer experience. Automate operational workflows, including incident detection, diagnosis, mitigation, and recovery. Improve the reliability, scalability, and efficiency of model deployments and the infrastructure that supports them. Build systems that support capacity planning, resource allocation, and infrastructure utilization. Partner with research, infrastructure, and product engineering teams to identify common chal

About the Team ChatGPT relies on a large and growing GPU fleet to serve inference workloads reliably and efficiently. Our team builds the software, tooling, and operational systems that help manage this fleet at scale. We work across production engineering, distributed systems, capacity management, and operational automation to improve reliability, reduce manual work, and make better use of available compute. About the Role We are looking for a software engineer with experience building or operating large-scale production systems. You will develop the systems that help manage the GPU fleet powering ChatGPT, including tooling for fleet health, capacity planning, operational automation, and incident response. You will work closely with infrastructure, research, and product engineering teams to improve reliability, developer productivity, and compute utilization. This role is a good fit for engineers who enjoy solving complex operational problems and building software that makes production infrastructure easier to run at scale. In This Role, You Will Build software and internal tools to manage large-scale GPU infrastructure supporting ChatGPT inference. Develop systems for capacity planning, fleet health monitoring, and resource utilization. Automate operational workflows, including incident detection, diagnosis, and response. Identify and address bottlenecks affecting fleet reliability, scalability, and performance. Partner with infrastructure, research, and product engineering teams to improve the compute platform. You Might Thrive in This Role If You Have experience operating large-scale production infrastructure, GPU clusters, or other compute-intensive distributed systems. Have a background in production engineering, site reliability engineering, infrastructure engineering, or platform engineering. Have built software that automates operational workflows and reduces manual work. Have worked with distributed infrastructure, cluster orchestration, or large-scale int

About the Team The compute infrastructure team runs the GPU fleet and large-scale compute clusters that serve the models backing ChatGPT and the API, while also supporting training workloads for our next generation models. We operate a large, modern GPU fleet and provide a unified platform for other OpenAI teams to seamlessly run production Applied AI and Research training workloads. We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth. About the Role You’ll own the hands-on and automation work that brings WAN, fiber, carrier, and cloud-interconnect circuits into service. Partner with network engineers, fiber providers, cloud service providers, colocation teams, and data-center technicians to move each connection from ordered and patched to verified, stable, and ready for handoff. You’ll own Layer 1 troubleshooting and circuit bring-up while building workflows that translate reliable system or model output into precise, approved technician actions, capture field feedback, and drive each connection to a green-port handoff. The right person combines strong physical-networking judgment with practical automation skills: patch-panel and port mappings, optics and light levels, provider coordination, structured operational data, API or scripting workflows, and human-in-the-loop LLM tooling. Responsibilities Own Layer 1 activation and restoration for carrier circuits, dark fiber, wavelengths, Ethernet handoffs, and dedicated cloud interconnects across data centers and points of presence. Reconcile complete A-side/Z-side as-builts: circuit IDs, LOAs/CFAs, carrier demarcations, MMR/ODF/MDF and patch-panel positions, fiber pairs, cross-connects, optics, and device ports. Investigate no-light, low-light, wrong-port, link-flap, and error-rate issues across providers and CSPs; isolate continuity, dirty connectors, polarity, incorrect patching

About the Team The compute infrastructure team runs the GPU fleet and large-scale compute clusters that serve the models backing ChatGPT and the API, while also supporting training workloads for our next generation models. We operate a large, modern GPU fleet and provide a unified platform for other OpenAI teams to seamlessly run production Applied AI and Research training workloads. We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth. About the Role You will be part of an engineer-first TPM team as a Technical Program Manager for Compute Infrastructure who owns the end-to-end delivery of large-scale GPU clusters, partnering with engineers to bring clusters online across external providers and partners. You’ll run a broad, parallel portfolio spanning hardware, networking, power, and cooling—driving execution, risk management, and crisp alignment from working teams through leadership to deliver production-ready capacity at scale. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Lead end-to-end delivery of both New Compute SKUs and large-scale GPU clusters across an external partner ecosystem while supporting capacity planning for training and inference. Ability to contextually drive multi-threaded bring-up programs spanning hardware, networking, power, and cooling—owning plans, dependencies, and critical paths. Interface with chip providers to derisk long-term onboarding to new hardware platforms by working across kernels, comms, hardware, and scheduling engineering teams. Build and operationalize program mechanisms (roadmaps, milestones, risk registers, runbooks) that make delivery predictable at massive scale. Partner with engineering to improve cluster turn-up reliability, repeatability, and automation

About the Role We are seeking a Strategic Finance Lead for our GPU compute fleet. In this role, you will be a key partner to our inference and infrastructure teams, providing financial expertise to optimize our compute investments and drive capacity decisions. In this role, you'll develop deep expertise in the economics of AI compute, from token-level serving cost to the long-range financial planning of our GPU fleet. You'll build the models that inform Perplexity's capacity and make-versus-buy decisions, own the internal-cost analysis that underpins internal and external token pricing, and translate complex infrastructure dynamics into clear financial narratives for leadership. This is a high-impact role for someone who thrives at the intersection of finance and infrastructure, and who is energized by building frameworks from scratch in a fast-moving environment. This position is based in San Francisco and requires in-person attendance 2-3 days per week. Key Responsibilities Finance lead for GPU compute spend, including budgeting, monthly forecasting, variance analysis, and financial plan maintenance Build and maintain detailed bottoms-up financial models for the GPU fleet, including capacity forecasts, cost driver analyses, and investment scenario modeling Develop deep expertise in GPU vendor contracts, pricing structures, and cost drivers, and surface optimization opportunities across the fleet Serve as subject matter expert for Perplexity's compute capacity plan, owning source of truth on utilization, committed-versus-consumed spend, and capacity by vendor and cluster Build internal token-cost curves distinguishing marginal from fully loaded serving cost, and translate them into internal and external token pricing Analyze the ROI of in-house inference and training, including opportunity cost across chip types, cluster configurations, and workloads, distilled into a framework for capacity deployment Partner closely with inference and infrastructure engineering to

About Us At BGBx , we're driven by a simple idea: breakthrough thinking creates breakthrough impact. As an independent commercial solutions partner to pharmaceutical and life science companies, we bring together strategists, scientists, communicators, creatives, technologists, and data experts to tackle some of healthcare's most important challenges. Our teams work across consulting and communications to help clients shape strategy, launch innovations, engage stakeholders, and drive meaningful results throughout the product lifecycle. The work is complex, fast-moving, and deeply connected to improving lives, creating opportunities for curious minds to make a real difference every day. If you're energized by collaboration, inspired by innovation, and motivated by work that matters, you'll find a place to grow, contribute, and make a meaningful impact at BGBx. Position Overview We are looking for an AI Engineer with 3-5 years of experience to help maintain and evolve our fleet of AI agents, LLM-as-a-judge systems, and other AI-powered tools used by healthcare, life sciences, and marketing teams. This role will work closely with senior engineers on graph design and with product and research teams to define what “a good answer” means, including grounding, coverage, and citation honesty. The AI Engineer will contribute to the continued development of these systems by improving tools, retrieval and citations, agent architecture, and overall system speed, cost, and accuracy. They will also support evaluations and experiments that help us understand and improve system performance. We are looking for someone with strong Python skills and a solid understanding of LLM systems who is excited to continue growing their technical depth. At this level, the engineer should be comfortable owning a component of a production service from design through testing and review, while partnering with more senior engineers on b

In this role, you will: Own diagnostic deployment for the current service vehicle fleet and successor platforms, from requirement definition through release into the operating fleet, and foster design for diagnostics with system, firmware, and software teams. Improve diagnostic quality to reduce time-to-diagnose in the fleet. Drive fault isolation accuracy and resolution so a triggered fault points to the right component and repair activity. Close end-to-end diagnostic gaps from requirement through V&V. Own the diagnostic requirements set, maintain traceability between requirements, implementation, test coverage, and in-fleet performance, and lead design and test reviews to catch gaps early. Define and deploy service actions tied to diagnostic outcomes. Partner with service engineering and fleet operations to ensure each fault maps to an actionable, verified repair, and confirm effectiveness using fleet data. Identify and develop prognostic features that predict component and system issues ahead of failure, translating fleet telemetry into monitoring strategies that reduce unplanned downtime, and report diagnostic health and gap closure status to cross-functional and program leadership. Develop metrics to measure diagnostic success and use them to identify system and process gaps, driving continuous improvement across the diagnostic lifecycle. Qualifications Bachelor's or Master's of Science in Electrical, Mechanical, Systems, or Computer Engineering. 10+ years of experience in vehicle or system diagnostics, with direct experience supporting a production or operating fleet. Demonstrated experience improving diagnostic quality and fault isolation accuracy using field data, with measurable reduction in time-to-diagnose. Working knowledge of automotive serial communication protocols (CAN, CAN-FD, LIN, Automotive Ethernet) and diagnostic protocols and standard

Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of artificial intelligence, robotics, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We're looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team. As a Staff Compute & Controls Systems Engineer in Advanced Hardware Engineering (AHE), you will be responsible for developing and documenting requirements for compute and control system modules, while also driving key functional safety analyses for our compute platforms. You will work across departments at Zoox to build a system-level understanding of how AHE hardware enables critical operational functions, and you will lead the Compute Safety Analysis (FMEDA) that ensures those functions fail safely. You will work closely with hardware and software architects, and with design, safety, manufacturing, and test engineers, to ensure requirements are verified throughout the engineering development process. Your assessments will be critical to meeting milestones as we develop new generations of hardware. In this role, you will: Apply compute, controls, and systems engineering principles to define traceable computing requirements from product goals Engage with product, HW/SW engineering, production, safety, and business teams to author detailed requirements for compute and control modules. Analyze tradeoffs and drive consensus with product, hardware, and software teams to converge on compute architecture for current and new vehicle platforms. Gather and analyze critical data to define metrics, qualify systems for release, and influence system design decisions based on evidence-based insights. Create and maintain clear, precise documentation to support system designs, architectural decisions, an

About the Team LCSP is DoorDash’s entrepreneur-led fleet model: independent partners hire, train, and manage courier fleets that fulfill DoorDash orders. The program has proven product-market fit and is now scaling with a path toward a much larger network. This hire would stand up the Account Management function for LCSP and own the strategy for bringing partners in at scale. About the Role For this role the Manager would be the person who builds the Account Management function — not just covering a book of partners, but designing how DoorDash sources, onboards, and manages LCSP entrepreneurs as volume grows. In the near term that includes overseeing entrepreneur onboarding and recruitment of business partners. In the longer-term the goal is to build those out as their own functions, with AM remaining the owner of the ongoing partner relationship. The primary responsibilities include: Stand up Account Management for LCSP: cadences, partner health, performance incentives, and the playbook for a scaling Own the strategy for onboarding partners — how we find, launch, and ramp without the process breaking as we scale Oversee onboarding and sales in the near term, with the mandate to build that function out over time Be the primary DoorDash counterpart for LCSP entrepreneurs after launch Partner with operators on performance, business health and incentive outcomes Use DoorDash ecosystem knowledge to unblock partners, pulling in S&O, product, support, and legal as needed. You will report to the Senior Manager on our Local Commerce Service Provider (LCSP) team in our Dasher & Logistics organization. You’re excited about this opportunity because you will… Be responsible for your own book of work as well as designing how its managedBuild scalable solutions with our partners - we want to build with them and leverage their professional expertise and feedbackBe part of a flat structure and pave the way for LCSP expansion Learn how the

Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team. As the Senior Technical Program Manager (TPM) for Vehicle Attributes, you will be the operational engine and technical coordinator behind vehicle attribute development (NVH, sound and voice, ride and handling, etc.). Partnering with a world-class team of robotics, controls, and vehicle engineers, you won’t just track status — you will anticipate risks, engineer frameworks that optimize development velocity, and bridge the gap between complex engineering and executive strategy. In this role you will: Own the end-to-end delivery of critical vehicle attribute milestones, proactively clearing technical bottlenecks to keep programs on track. Partner with attribute and test leads to surface local risks (e.g., vehicle downtime, hardware shortages) and architect immediate recovery plans. Design and maintain lightweight JIRA workflows, sprint cadences, and reporting rituals that maximize engineering focus and minimize admin debt. <li style="font-s

About the Team The Hardware Health and Observability team owns the end-to-end health lifecycle of OpenAI’s global compute fleet. Our mission is to maximize healthy, usable compute across accelerator vendors, generations, cloud providers, and regions through reliable health signals, automated remediation, and scalable operational tooling. We build the systems that observe, detect, remediate, and verify hardware issues across GPUs, CPUs, networking, and platform infrastructure, enabling frontier model training and inference workloads to run reliably at hyperscale. We are the last line of defense for the success of OAI’s production and research workloads. About the Role On the Hardware Health and Observability team, you’ll build critical infrastructure that keeps OpenAI’s largest compute clusters healthy and operational at scale. Even small numbers of unhealthy systems can impact large-scale training and inference workloads. This team focuses on minimizing downtime, improving fleet efficiency, and ensuring compute resources remain continuously available to researchers and product teams. Engineers on this team own problems end-to-end, from defining health signals and debugging failures to building automated remediation systems that operate across millions of GPUs globally. In this role, you will: Define and maintain health signals across GPUs, CPUs, networking, and platform infrastructure. Build and evolve health checks that detect, remediate, and verify failures at scale. Ensure critical health checks execute with minimal latency to maximize workload uptime. Investigate hardware failures and system-level issues across large-scale compute environments. Own node lifecycle workflows including drain, quarantine, repair, RMA, and return-to-service processes. Build automation and tooling that enables global cluster management with minimal manual intervention. Partner with workload, reliability, and provider teams to integrate health signals into training and inference system

We are seeking a strategic and execution-oriented Logistics & Operations Planning (S&OP) Manager to build and lead our process that aligns fleet demand with manufacturing, supply chain, operations, and financial plans. This role will sit within the Logistics organization and serve as a key cross-functional integrator, ensuring that demand, supply, capacity, inventory, and cost assumptions are connected through a single planning process that enables clear decisions and disciplined execution. This individual will establish the S&OP governance, cadence, and decision-making framework needed to align business priorities with operational capabilities. The ideal candidate will bring strong experience in planning, operations, and cross-functional leadership, with the ability to translate dynamic business needs into a scalable and effective planning rhythm. In this role, you will: Design and lead the company-wide S&OP process to align fleet demand, production, supply readiness, inventory, and financial plans. Establish and run the monthly planning cadence, including demand review, supply review, scenario planning, and executive alignment. Partner with Logistics, Manufacturing, Finance, Supply Chain, and Operations teams to maintain a single integrated view of priorities, risks, and constraints. Drive scenario analysis and trade-off decisions related to demand changes, supply constraints, capacity, inventory, and cost. Develop and track key planning metrics such as forecast accuracy, plan attainment, inventory health, service readiness, and cost control. Create executive-ready communications and recommendations while driving continuous improvement in planning processes, tools, and reporting. Qualifications Bachelor’s degree in Supply Chain, Operations, Engineering, Business, Finance, or a related field. 10+ years of

Role Overview: As a Senior Technical Product Marketing Manager, Edge, at Defense Unicorns, you will lead the product marketing strategy for UDS Fleet and help drive its growth and adoption across defense edge environments. You will report to the Head of Product Marketing and work closely with Product, Engineering, Growth, and Executive leadership to strengthen UDS Fleet’s market position and bring new features and capabilities to customers. This is an execution-heavy role with significant ownership across the product lifecycle. You will define positioning and messaging, develop technical content and demonstrations, enable Growth teams, support feature launches, and help shape how UDS Fleet is packaged, adopted, and communicated. You will also serve as a trusted product expert and evangelist, ensuring our product narratives reflect real mission needs and clearly communicate the value of deploying, managing, and operating software across distributed and edge environments. Responsibilities: The listed responsibilities are not exhaustive, and additional responsibilities may be assigned based on the evolving needs of the organization. We are seeking a dynamic individual who can operate effectively amid ambiguity, establish structure for a new market offering, and take on new responsibilities as the capability and organization evolve. Develop and execute end-to-end go-to-market strategies for new products and features Define target audiences, positioning, messaging, and product narratives Support product naming, launch planning, adoption, and post-launch optimization Establish the go-to-market foundation for products entering new or undefined markets Establish deep technical expertise across the Defense Unicorns product portfolio Translate complex technical concepts into clear and differentiated customer value Create solution briefs, white papers, technical blogs, and web content Develop presentations, launch materials, videos, demos, and use-case narratives Partner with