Fleet Coordinator in United States

Datadog is looking for a Senior Product Manager to help lead the evolution of our fleet and lifecycle management capability, the product surface that gives customers visibility into, and control over, the observability software running across their infrastructure. This capability manages the deployment lifecycle for core observability agents and OpenTelemetry collectors running on customer hosts and containers. The Senior PM will expand the scope of fleet capability to additional Datadog software components, making it the single place customers go to see everything running in their environment, at any version, in any deployment model, and to manage it remotely and safely at scale, for both human operators and, increasingly, AI agents acting on their behalf. This is a high-visibility, cross-functional role. You'll partner with multiple engineering teams and be responsible for defining and delivering a coherent, unified fleet experience across UI, API, and MCP for customers. At Datadog, we place value in our office culture - the relationships and collaboration it builds, and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You'll Do Own and evolve the product vision and roadmap for a unified fleet and lifecycle management capability spanning multiple product lines and deployment models. Define what "managed" means for each new software component as it's brought into fleet, balancing consistency of experience with the realities of each component's operational model. Drive a phased expansion plan, sequencing new components into fleet based on customer value, technical complexity, and dependency readiness. Partner closely with engineering leads across several teams to align on shared architecture principles to support disparate software components. Represent the voice of the customer for a capability that must work equally well for human operators using a UI and for AI

About the team The Fleet team at OpenAI supports the computing environment that powers our cutting-edge research and product development. We oversee large-scale systems that span data centers, GPUs, networking, and more, ensuring high availability, performance, and efficiency. Our work enables OpenAI’s models to operate seamlessly at scale, supporting both internal research and external products like ChatGPT. We prioritize safety, reliability, and responsible AI deployment over unchecked growth. About the role As a software engineer on the Fleet Hardware team, you will be responsible for the reliability and uptime of all of OpenAI’s compute fleet. Minimizing hardware failure is key to research training progress and stable services, as even a single hardware hiccup can cause significant disruptions. With increasingly large supercomputers, the stakes continue to rise. Being at the forefront of technology means that we are often the pioneers in troubleshooting these state-of-the-art systems at scale. This is a unique opportunity to work with cutting-edge technologies and devise innovative solutions to maintain the health and efficiency of our supercomputing infrastructure. Our team empowers strong engineers with a high degree of autonomy and ownership, as well as ability to effect change. This role will require a keen focus on system-level comprehensive investigations and the development of automated solutions. We want people who go deep on problems, investigate as thoroughly as possible, and build automation for detection and remediation at scale. In this role, you will: Build and maintain automation systems for provisioning and managing server fleets. Develop tools to monitor server health, performance, and lifecycle events. Collaborate with clusters, networking, and infrastructure teams. Partner with external operators to ensure a high level of quality. Identify and fix performance bottlenecks and inefficiencies. Continuously improve automation to reduce manual work

This role will support the fleet infrastructure team at OpenAI. The fleet team focuses on running the world’s largest, most reliable, and frictionless GPU fleet to support OpenAI’s general purpose model training and deployment. Work on this team ranges from Maximizing GPUs doing useful work by building user-friendly scheduling and quota systems Running a reliable and low maintenance platform by building push-button automation for kubernetes cluster provisioning and upgrades Supporting research workflows with service frameworks and deployment systems Ensuring fast model startup times though high performance snapshot delivery across blob storage down to hardware caching Much more! About the Role As an engineer within Fleet infrastructure, you will design, write, deploy, and operate infrastructure systems for model deployment and training on one of the world’s largest GPU fleet. The scale is immense, the timelines are tight, and the organization is moving fast; this is an opportunity to shape a critical system in support of OpenAI's mission to advance AI capabilities responsibly. 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: Design, implement and operate components of our compute fleet including job scheduling, cluster management, snapshot delivery, and CI/CD systems. Interface with researchers and product teams to understand workload requirements Collaborate with hardware, infrastructure, and business teams to provide a high utilization and high reliability service You might thrive in this role if you: Have experience with hyperscale compute systems Possess strong programming skills Have experience working in public clouds (especially Azure) Have experience working in Kubernetes Execution focused mentality paired with a rigorous focus on user requirements As a bonus, have an understanding of AI/ML workloads About OpenAI OpenAI is an AI resea

Hyliion is committed to creating innovative solutions that enable clean, flexible and affordable electricity production. The Company’s primary focus is to develop distributed power generators that can operate on various fuel sources to future-proof against an ever-changing energy economy. Job Purpose The Senior Manager, Additive Fleet is responsible for the day-to-day performance of Hyliion's laser powder bed fusion (LPBF) printer fleet in Austin, which produces the complex, high-density metal heat exchanger hardware in the KARNO Core. This hardware can only be produced through metal additive manufacturing, so Hyliion's ability to build KARNO at scale depends directly on the health, uptime, and throughput of the fleet. The role leads the technicians and operators who run and maintain the machines, and owns preventive maintenance strategy, machine health monitoring, and decisions on hardware and software upgrades. The fleet runs the full range of Colibrium Additive LPBF platforms, including new machine technology being adopted in real time, and a primary focus is reducing machine-to-machine variation and building repeatable processes across machine models. This is a hands-on leadership role with regular time on the shop floor, and its scope will grow as Hyliion's print capacity scales. AI at Hyliion At Hyliion, AI is core to how we work. We equip every team member with leading AI tools and count on you to use them — to move faster, solve harder problems, and help us realize the full potential of KARNO technology for the world. Duties and Responsibilities Own fleet uptime and drive continuous improvement in machine-to-machine consistency across Hyliion's LPBF printer fleet. Build and maintain a preventive maintenance program across all machines to identify failure modes before they cause downtime. Lead, schedule, and develop the team of technicians and operators who run and maintain the fleet. Develop machine health monitoring us

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. As a Principal Software Engineer leading Fleet Management, you will be the overall technical lead across three pods and the person who sets the technical direction for the fleet management layer of Roblox. This is a hands-on, deeply technical leadership role that owns all of Roblox's compute capacity end to end: from low-level provisioning and the data plane, up through the control planes that operate it, and all the way to the UI and internal-facing products that let teams self-serve capacity. Your org centralizes security, maintenance operations, and the uptime of every Roblox Kubernetes cluster, and governs the internal customer contracts that drive automation across the fleet spanning Roblox data centers and cloud providers. You will guide architecture, raise the engineering bar, and make sure compute capacity supply and demand stay in balance as the fleet grows. You will: Serve as the overall technical lead for three Fleet Management pods, setting and aligning the technical direction across low-level provisioning, the data plane, and the control plane and product surfaces above them. Architect the declarative, Kubernetes-style control planes that operate Roblox's compute fleet across o

About the Team Full Stack engineers within the Fleet Scheduling team are dedicated to building intuitive and scalable interfaces that empower researchers to efficiently manage AI workloads across some of the largest supercomputers in the world. Our focus is on developing robust, high-performance systems that provide real-time insights, resource tracking, and seamless interaction with complex infrastructure. We aim to optimize resource allocation, minimize operational overhead, and create user-friendly tools that enhance researcher productivity and system transparency. About the Role You will design, develop, and operate web-based systems that provide a powerful and intuitive interface to OpenAI’s supercomputing clusters. You will collaborate closely with researcher, product and infrastructure teams to deliver scalable solutions that enable seamless monitoring, job scheduling, and resource management. This is an opportunity to work at the cutting edge of AI infrastructure, designing tools that scale to exascale workloads while maintaining usability and performance. 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: Design and develop full-stack web applications to track, monitor, and manage large-scale AI workloads in real time. Collaborate with researchers and infrastructure teams to translate complex operational needs into intuitive UIs and scalable backends. Build data visualization tools (e.g., Gantt charts, dashboards) to provide insights into job scheduling and resource allocation. Optimize backend services to handle massive data throughput while ensuring low-latency performance and high availability. Implement frontend components that provide seamless interactions with scheduling, storage, and compute systems. Ensure system security, reliability, and scalability across globally distributed supercomputing infrastructure. You might thrive i

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We’re looking for a Rack Power Engineer with deep expertise in high-power conversion and distribution to design, qualify, and support power systems for AI supercomputers. You will own rack power solutions—including power shelves, AC/DC rectifiers, power supply units (PSUs), power management controllers (PMCs), and high-current distribution—from requirements and supplier development through deployment. You will also monitor fleet rack power health, lead debugging and root-cause investigations, and drive improvements into hardware, firmware, and qualification coverage. 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: Own rack power architecture and requirements for high-power AI supercomputing systems, including power budgets, AC input interfaces, DC distribution, redundancy, efficiency, serviceability, and integration with data center infrastructure. Drive the design and supplier development of power shelves, rectifiers, PSUs, PMCs, busbars, connectors, and protection circuits. Review electrical designs and control behavior, and evaluate performance, cost, reliability, and availability trade-offs. Define and execute component, shelf, and rack qualification plans covering load transients, current sharing, hot-swap, startup and shutdown, redundancy failover, fault protection and recovery, thermal limits, and AC disturbances and ride-through

About the team The Fleet team at OpenAI supports the computing environment that powers our cutting-edge research and product development. We oversee large-scale systems that span data centers, GPUs, networking, and more, ensuring high availability, performance, and efficiency. Our work enables OpenAI’s models to operate seamlessly at scale, supporting both internal research and external products like ChatGPT. We prioritize safety, reliability, and responsible AI deployment over unchecked growth. About the role As a software engineer on the Fleet High Performance Computing (HPC) team, you will be responsible for the reliability and uptime of all of OpenAI’s compute fleet. Minimizing hardware failure is key to research training progress and stable services, as even a single hardware hiccup can cause significant disruptions. With increasingly large supercomputers, the stakes continue to rise. Being at the forefront of technology means that we are often the pioneers in troubleshooting these state-of-the-art systems at scale. This is a unique opportunity to work with cutting-edge technologies and devise innovative solutions to maintain the health and efficiency of our supercomputing infrastructure. Our team empowers strong engineers with a high degree of autonomy and ownership, as well as ability to effect change. This role will require a keen focus on system-level comprehensive investigations and the development of automated solutions. We want people who go deep on problems, investigate as thoroughly as possible, and build automation for detection and remediation at scale. In this role, you will: Build and maintain automation systems for provisioning and managing server fleets. Develop tools to monitor server health, performance, and lifecycle events. Collaborate with clusters, networking, and infrastructure teams. Partner with external operators to ensure a high level of quality. Identify and fix performance bottlenecks and inefficiencies. Continuously improve automati

About the Team The Fleet team builds core components to enable productive research from small to state of the art scale across OpenAI, with the goal of accelerating progress towards AGI. We frequently collaborate with other teams to speed up the development of new state-of-the-art capabilities. About the Role As we scale up with more researchers and engineers joining OpenAI, we seek a pragmatic and passionate engineer with a strong focus on the development experience for both engineers and scientists. In this role, you will be responsible for building and maintaining systems that allow our research + engineering organization to iteratively develop, test, and deploy new features reliably, with high velocity, and with a frictionless and fast development cycle. You will help oversee and drive to the vision of how we should build, test and deploy software. You will drive the design of our continuous integration pipelines, testing infrastructure, training and support around our build system. Our current environment relies heavily on Python, Rust, and C++, which you will take ownership of and strive to transform into a state of the art development experience for research. Ultimately, your role will be to provide the necessary tools and metrics to support our fast-paced culture and ensure a stable, scalable platform for growth, while also fostering a seamless and low friction experience for OpenAI’s research. This role is based in San Francisco, CA. For a San Francisco role, we use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. You might thrive in this role if you: Have supported large monorepo development and deployment before Are a proficient Python programmer working in large monorepos Are proficient with Docker and Kubernetes Experienced in CI/CD About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boun

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 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

NVIDIA’s DGX Cloud organization is seeking a Senior Data Engineer to become part of its data team! We develop the reliable data foundation that supports fleet health, capacity, utilization, cost, reliability, and operational decision-making throughout DGX Cloud. Our platform supports engineering, operations, finance, and product teams managing and expanding large GPU fleets across cloud service providers and NVIDIA Cloud Partners. We are looking for a practical engineer and technical lead to take charge of a key part of the Navigator data platform. We develop the systems that transform distributed infrastructure telemetry and operational data into dependable, managed data products that support fleet health, capacity, utilization, cost, and operational decisions. We are seeking a hands-on, platform-minded engineer to build and evolve the systems that turn distributed infrastructure telemetry and operational data into reliable, governed data products. You will work across ingestion, transformation, data quality, platform architecture, security, observability, and self-service consumption to help make Navigator and the DGXC data platform a dependable source of truth. We do expect strong engineering fundamentals, experience operating production systems, and the ability to learn new platforms and domains quickly. What you'll be doing: Own systems end to end. For example, work from ambiguous customer and operational needs through architecture, implementation, deployment, observability, incident response, and ongoing support. Construct data pipelines and products. Such as designing and maintain batch and streaming ingestion, transformation, reconciliation, and serving paths for fleet, capacity, utilization, cost, scheduling, and operational telemetry. Build shared libraries, workflow and DAG or equivalent experience abstractions to evolve the data platform. Develop deployment tooling, data

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role OpenAI's Hardware organization builds supercompute platforms from silicon and boards to full rack-scale systems to power advanced AI workloads. This role owns end-to-end quality for high-speed interconnect hardware across the product lifecycle: early design influence, supplier/contract manufacturer readiness, qualification, ramp, and fleet quality in lab and data center environments. You will be the quality lead for advanced interconnect components and assemblies, including high-speed copper cables, cable cartridges, patch panels, backplane/cable-backplane solutions, high-speed connectors, and related electro-mechanical interfaces. You will partner closely with electrical, mechanical, SI/PI, systems, reliability, operations, and external vendors to prevent escapes and drive rapid, data-driven containment and corrective action. In this role you will: Own quality for advanced interconnect components and assemblies: high-speed connectors, high-speed copper cables, cable cartridges (e.g., cable cassette style assemblies), patch panels & optics, and backplane/cable-backplane interconnect solutions. Drive quality-by-design: participate in design reviews, DFM/DFx, tolerance stacks, material and plating selections, connector mating strategy, strain relief, and assembly methods to reduce variation and field failures. Define and track quality and reliability metrics (DPPM, yield, escapes, RMA/FRACAS trends, Cpk/Ppk where applicable) for interconnects across NPI and m

About the Team The Release Engineer team is responsible for building and maintaining the systems that power software delivery—from CI/CD pipelines and artifact management to release automation and fleet telemetry. We ensure software across bootloaders, firmware, operating systems, and cloud services is built reproducibly, validated rigorously, and released safely at scale. About the Role As a Release Engineer, you’ll design, build, and operate release infrastructure that enables reliable, secure, and traceable software delivery across complex multi-component systems. You’ll partner closely with embedded, cloud, and QA teams to ensure that every build—from development to OTA deployment—is fast, verifiable, and production-ready. We’re looking for engineers who take pride in automation, build reproducibility, and system reliability—and who enjoy building the connective tissue that allows hardware and software to ship together seamlessly. This role is based in San Francisco, CA. We use a hybrid work model of four days in the office per week and offer relocation assistance to new employees. In this role, you will: Design and operate CI/CD pipelines for multi-component builds (bootloader, firmware, OS images, backend, companion apps) using hermetic toolchains. Define versioning and branching strategies; automate promotions, changelogs, and artifact retention. Integrate unit, integration, and hardware-in-the-loop (HIL) test results; quarantine flaky tests, auto-bisect failures, and block unsafe promotions. Build A/B OTA update flows with verity and health checks; run staged rollouts and canaries; implement safe rollback and roll-forward strategies. Implement code signing for binaries and firmware, generate SBOMs, run vulnerability scanning, and attach build attestations and provenance. Manage dashboards and alerts for build health, promotion latency, failure rates, and fleet update telemetry. You might thrive in this role if you: Have experience building and operating buil

About the Team Our team analyzes inference stack performance across the application, model, and fleet layers to identify bottlenecks and drive faster, cheaper inference. We combine systems profiling, benchmarking, and analysis to understand where time and cost are spent, then turn that understanding into performance optimizations and models that project performance and capacity needs for future launches. About the Role In this role, you will model inference performance across application, model, and fleet layers with higher fidelity. You will build cost-to-serve estimates from microbenchmarks and create tools that help cross-functional teams reason about latency, capacity, utilization, and cost tradeoffs. In this role, you will Build and refine performance models that translate microbenchmark results into cost-to-serve estimates. Analyze inference workloads end to end across applications, models, and fleet infrastructure. Enhance tooling to identify bottlenecks across layers for latency and throughput. Partner with other teams to turn performance insights into concrete improvements and project how future changes affect inference. You might thrive in this role if you: Enjoy reasoning from first principles about distributed systems, model inference, and hardware efficiency. Are comfortable working across abstraction layers, from application behavior to kernels, accelerators, networking, and fleet scheduling. Have deep expertise with performance profiling, benchmarking, analysis, and optimization. Enjoy collaborating with engineering and research teams to improve real production systems. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve o