Fleet Associate Jobs

The Undersea Systems Division at Leidos currently has an opening for a Systems Engineer to support a customer site in San Diego, California. This is an exciting opportunity to apply systems engineering, naval operations, test and evaluation, and technical project-management experience in support of large-scale fleet activities and critical maritime missions. The selected candidate will serve as the onsite Systems Engineer and senior technical representative, working closely with the Leidos Project Manager, customer leadership, engineers, fleet personnel, operational stakeholders, and government representatives. The position will support day-to-day onsite execution of program activities while also contributing directly to systems engineering, integration, test, and evaluation efforts. Primary Responsibilities: Serve as the onsite senior technical representative for Leidos at the customer site in San Diego. Work side by side with customer leadership and technical personnel to support day-to-day program execution and mission priorities. Support the Leidos Project Manager with program planning, execution, customer coordination, schedule management, risk management, and delivery of program commitments. Lead and coordinate engineering activities across multiple technical disciplines and stakeholder organizations. Translate customer operational needs into technically sound engineering approaches, priorities, requirements, and recommendations. Coordinate with engineers, subcontractors, fleet personnel, operators, government stakeholders, and program leadership to resolve technical and operational issues. Track technical scope, milestones, schedules, deliverables, action items, risks, issues, dependencies, and decisions. Maintain project-management artifacts, including project plans, schedules, milestone trackers, risk and issue registers, action-item logs, and status rep

The Undersea Systems Division at Leidos currently has an opening for a Technical Program Lead to support a customer site in San Diego, California. This is an exciting opportunity to apply systems engineering, naval operations, test and evaluation, and technical project-management experience in support of large-scale fleet activities and critical maritime missions. The selected candidate will serve as the onsite Technical Program Led and senior technical representative, working closely with the Leidos Project Manager, customer leadership, engineers, fleet personnel, operational stakeholders, and government representatives. The position will support day-to-day onsite execution of program activities while also contributing directly to systems engineering, integration, test, and evaluation efforts. Primary Responsibilities: Serve as the onsite senior technical representative for Leidos at the customer site in San Diego. Work side by side with customer leadership and technical personnel to support day-to-day program execution and mission priorities. Support the Leidos Project Manager with program planning, execution, customer coordination, schedule management, risk management, and delivery of program commitments. Lead and coordinate engineering activities across multiple technical disciplines and stakeholder organizations. Translate customer operational needs into technically sound engineering approaches, priorities, requirements, and recommendations. Coordinate with engineers, subcontractors, fleet personnel, operators, government stakeholders, and program leadership to resolve technical and operational issues. Track technical scope, milestones, schedules, deliverables, action items, risks, issues, dependencies, and decisions. Maintain project-management artifacts, including project plans, schedules, milestone trackers, risk and issue registers, action-item logs, and

Job Title Sales, Strategic Accounts Director - Image Guided Therapy Systems (Southeast/Mid-Atlantic) Job Description The Director of IGT Strategic Accounts will work with 2 IGTS Districts and be responsible for order and revenue growth at a targeted set of named accounts. Your primary responsibilities will be to act strategically and grow Philips Marketshare by focusing on: executing fleet replacement plans of Philips Install Base accelerating competitive replacements at accounts where Philips is not installed (red accounts). In this role, you will work closely with the Philips IGT Sales and Marketing teams, you will build deep customer relationships at multiple touchpoints throughout your assigned health systems, all while developing and deploying strategies to solve interesting hospital challenges to help IGT continue to win. Your role Deliver agreed-upon growth in targeted accounts, and contribute to the overall sales results of the covered districts. Actively manage, monitor and continually improve the team’s overall sales process to ensure a successful productivity ramp in order to exceed revenue and commercial goals. Analyze and quantify equipment replacement potential at targeted accounts, assess regional market conditions, and use this to create and execute strategies to help grow Philips share Partner with CADs and ECEs to relentlessly assess and build out IDN and GPO sales strategies that help the district grow Philips IB or replace competitive IB Track and analyze customer performance to identify positive or negative trends, all while looking for opportunities to find mutually beneficial areas for growth. Able to quickly digest data, identify trends, and turn them into proactive strategies Ability to both work with and lead the IGT field sales teams in a matrix environ

About the Role OpenAI’s Industrial Compute organization is responsible for ensuring our compute infrastructure scales efficiently to support millions of users and increasingly sophisticated AI models. We’re looking for a Data Scientist to partner closely with Capacity Systems Engineering, Infrastructure, Product, and Research to optimize inference capacity across our global GPU fleet. This role combines statistical modeling, large-scale data analysis, forecasting, and systems thinking to drive critical decisions around infrastructure investments, performance-efficiency trade-offs, and customer experience. You’ll transform complex operational data into actionable insights that directly influence how OpenAI allocates and scales one of the world’s largest AI compute environments. Key Responsibilities Build statistical and machine learning models to profile and improve GPU utilization, latency, throughput, and overall fleet efficiency. Develop forecasting models for inference demand across products, regions, and model families. Analyze production workloads to identify latency bottlenecks and capacity constraints, highlighting optimization opportunities. Partner with Capacity Systems Engineering to inform infrastructure planning and long-term GPU investment strategies. Design experiments and simulations to evaluate scheduling policies, serving strategies, and infrastructure tradeoffs. Build dashboards and operational metrics that enable leadership to make data-driven capacity decisions. Collaborate with Product, Research, Finance, and Infrastructure teams to align compute planning with business growth and model roadmaps. Communicate technical findings clearly to both engineering teams and executive leadership. Qualifications MS or PhD in Statistics, Computer Science, Operations Research, Applied Mathematics, Economics, or related quantitative discipline (or equivalent industry experience). 5+ years of experience working in the infrastructure data science space. Strong ex

About the Team DoorDash Labs is a team within DoorDash building autonomous delivery robots and other autonomy solutions from the ground up for DoorDash's core delivery platform. If you have a passion for applying robotics solutions to a service loved by millions of people, then we want to talk to you! About the Role As Team Lead, Autonomy Tech Support, you will lead the day-to-day execution and development of the Mexico City Autonomy Tech Support team while maintaining a strong understanding of its technical workflows. You will set a high bar for troubleshooting, escalation quality, documentation, and operational readiness as the autonomous delivery fleet continues to scale. You will report into the Manager, Autonomy Tech Support on our Autonomy Tech Support team in our DoorDash Labs organization. Work model: 100% in-office in Coyoacán, Mexico City, Open for T4 levels, Across all LOBS. You’re excited about this opportunity because you will… Lead the day-to-day execution of the CDMX ATS team, including coverage, workload, priorities, and high-impact fleet issues. Coach and develop ATS Specialists through regular feedback, technical coaching, and hands-on support. Maintain a high bar across troubleshooting, escalation quality, Failure Mode execution, documentation, and cross-functional communication. Serve as the first leadership escalation point in CDMX for complex or high-urgency issues. Be a first line of defense for potential software regressions and emerging fleet-level trends, ensuring abnormal behavior is identified, validated, and escalated quickly. Lead AI adoption, integration, and enablement across the CDMX ATS team, ensuring AI capabilities are effectively incorporated into day-to-day workflows while identifying opportunities to improve troubleshooting, decision-making, and team efficiency. Partner with Operational Intelligence and Operational Reliability to ensure the team has the tools, systems, and processes needed to operate effectively and effic

As the Manager of Model Validation & Verification (VnV) for Behavior Autonomy, you will lead an engineering and data science team responsible for evaluating, benchmarking, and validating the machine learning models and behavioral algorithms that drive our autonomous vehicle (AV) prediction and planning stacks. You will own the statistical frameworks, offline/online evaluation metrics, and validation pipelines that ensure our behavioral models operate safely, comfortably, and predictably. Operating at the intersection of Data Science, Machine Learning, and Safety Engineering, you will partner closely with Autonomy Software, Prediction, Planner, and ML Operations teams to establish data driven release criteria for our AV fleet. In this role, you will: Team Leadership & Execution: Lead, mentor, and scale a high-performing team of Data Scientists, ML Validation Engineers, and Software Engineers while driving roadmaps, sprint execution, resource allocation, and high-throughput model releases with rigorous safety guardrails. Culture of Rigor: Foster a culture of statistical excellence, healthy skepticism, proactive risk tracking, and data-driven decision-making. Validation Strategy & Methodologies: Define and execute end-to-end validation strategies across offline evaluation, open/closed-loop simulation, and shadow-mode fleet benchmarking to ensure robust behavioral model performance. Statistical uncertainties, and regressions into clear, data-driven recommendations for release gating and executive leadership. Metrics, Release Gating & Rigor: Oversee metric development and standardization with System Safety and Autonomy teams, establishing quantitative go/no-go release criteria for Behavioral Planner and Prediction ML models while fostering statistical rigor and proactive risk management. Cross-Functional & Infrastructure Partnership: Partner closely with Planner, Prediction, MLOps

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Data and analytics are at the heart of Lyft’s products and decision-making. As a member of the Lyft Urban Solutions team, you will play a key role in shaping the future of bikeshare by leveraging data to operational needs and city partners. In this role, you will collaborate closely with Operations, Science, and Finance teams to drive data-driven strategies that align our operations with city transportation goals. You will work in a fast-paced environment where analytical insights directly impact fleet management, operator performance, pricing, rider accessibility, and long-term investments in bikesharing infrastructure. We’re looking for a passionate and driven Data Analyst Intern to tackle some of the most complex and impactful challenges in bikesharing. If you’re excited about shaping the future of urban mobility through data, we’d love to hear from you. Responsibilities: Partner with Operations, Engineering, Data Science & Analytics, Product, Finance and other cross-functional stakeholders to manage and update key business logic Develop frameworks and scalable processes to streamline reporting, drive decision-making and prioritization Define the metrics used to measure the success of strategic initiatives and system health; build dashboards to monitor metrics performance over time Work closely with cross-functional partners to deliver data quickly, reliably and accurately to our city partners Monitor and diagnose KPI performance and present findings to senior leadership Experience: Currently pursuing a Bachelors or Masters degree in mathematical sciences (Operations Research, Computer Science, Statistics, Applied Mathematics, Theoretical Physics, Behavioral Science, Electrical Engineering, etc.), Economics (Microeconomics Theory, Econometrics etc.), Data Engineering; or a related field;

Who we are Lab37 Robotics , is a technology company focused on the development and deployment of robots designed specifically for direct-to-customer food production. Our mission is to revolutionize the food industry by creating innovative robotic solutions that enhance efficiency, quality, and customer satisfaction. We are passionate about pushing the boundaries of technology to deliver cutting-edge products that meet the evolving needs of our clients. What you'll do Take on ownership of the cloud platform infra layer that powers Lab37's robotics fleet. This spans building Cloud infra, ML, ETL, CI/CD pipelines that turn raw robot telemetry into actionable analytics, managing data discovery, data lineage and contributing to models training pipelines on our robots, and ensuring the observability and reliability of the entire data stack. Work with a small, high-impact platform team to build the systems that every product team at Lab37 consumes — from data scientists training models, to kitchen operations teams viewing dashboards, to engineers deploying new ML models to robots in the field. Responsibilities Design, build, and maintain scalable ML infrastructure that abstracts away underlying storage, pipeline management, and repetitive environment setup tasks. Implement robust systems for automated model checkpointing, persistent metadata management, and experiment tracking across distributed training runs. Create self-service ML workflows and tooling that empower ML engineers and data scientists to focus on core logic, model architecture, and validation. Build and maintain automated ETL and data ingestion pipelines that stream and transform raw robot telemetry into clean datasets for training and analytics. Contribute to infrastructure-as-code (Terraform) and CI/CD automation for model deployment, data processing, and cloud services. Partner with cloud and embedded engineers to streamline model deployment to fleets of robots in the field and participate i

What we’re doing isn’t easy, but nothing worth doing ever is. Diligent builds helpful robots that work safely and autonomously in real world environments. We move quickly, solve messy problems, and care deeply about reliability at scale. As a Fleet Engineer, you'll own the reliability and continuous improvement of our deployed robotic fleet — leading hands-on investigations into how and why robots fail in the field, across the mobile base, charging/docking, motion and power, connectivity (modem), and sensor hardware. You'll combine remote data analysis with bench/lab failure analysis at our Austin HQ, turning field-technician reports and fleet data into clear problem statements, validated root causes, and corrective actions driven to closure with engineering, operations, manufacturing, and vendors. We are hiring a Lead Engineer, Issue Management & Triage to lead the systems, tooling, and team at the intersection of our Customers, Remote Operations Center (ROC), and Engineering. This is a highly technical, hands-on role focused on building the infrastructure that powers how we detect, triage, diagnose, and resolve issues across a deployed robotic fleet. You will work deeply with Engineering teams to design classification frameworks, build internal tools, and develop automation pipelines that improve reliability at scale. Location: Austin preferred, Remote possible (U.S.) Travel: if remote up to ~50% travel to Austin, TX (especially in the your first 90 days) What You’ll Do: Own Issue Management & Triage Systems Design and own end-to-end systems for issue intake, triage, and escalation. Define severity frameworks, SLAs, and ensure issues are consistently structured for engineering prioritization. Build Tools & Automation (Hands-On) Develop automation and pipelines to ingest, process, and classify operational data, reducing manual triage effort. Contribute directly to codebases (Python, backend services) and partner with Engineer

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

About the Role Together AI runs one of the largest GPU fleets in the world. The Infra Agent Systems team builds the software systems that power and automate that infrastructure. We develop production AI agents that diagnose hardware failures, investigate incidents, correlate signals across the fleet, and automate operational workflows. Alongside these agents, we build the platform they run on, including knowledge graphs, retrieval systems, orchestration frameworks, and developer tooling. You’ll work across two areas: Infrastructure Agent Systems — Build production AI agents that help operate our GPU fleet by diagnosing failures, investigating incidents, gathering evidence from live systems, and assisting with remediation. These agents are used every day by our infrastructure and datacenter teams through APIs, CLI, dashboards, and Slack. Core Agent Platform — Build the platform that powers these agents, including knowledge graphs, search and retrieval, orchestration, evaluation, and the tooling that enables agents to reason, act, and continuously improve. We’re working on something that hasn’t really been done before: building knowledge graphs and self-improving AI agents that understand, operate, and continuously improve large-scale AI infrastructure. This is an opportunity to work at the intersection of AI agents, distributed systems, infrastructure, and automation , solving challenging engineering problems with real production impact. There’s an enormous amount to build, learn, and shape as we define the future of autonomous infrastructure. Why this Role You’ll work on two hard problems at the same time: making AI agents trustworthy enough to operate production infrastructure, and building the knowledge, retrieval, and distributed systems that make those agents effective. You’ll have the opportunity to build foundational systems from the ground up, work on infrastructure at massive scale, and help define how self-improving AI agents operate real-world AI infrastru

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Baseten's Compute org is in hyper growth. As it scales, the systems and workflows that keep supply and demand balanced across our GPU fleet need to get more sophisticated, and this role exists to make sure they do. Compute sits at the center of how Baseten allocates, forecasts, and manages the capacity that powers every customer inference request. The team that supports this work, C3, runs on a mix of internal tooling, manual processes, and systems that haven't fully kept pace with the scale of the problem. This role exists to close that gap. You'll design, build, and ship AI-powered workflows that give the Compute and C3 teams real leverage, automating the manual, repetitive, and error-prone parts of the capacity lifecycle so the team can focus on judgment calls that actually need a human. We want someone who can walk in, audit what exists today, identify what's missing or broken, and start shipping fast. You know when to reach for an existing internal tool and when to build something custom in Claude Code. You think two to three steps ahead about how the thing you build today fits into the broader capacity systems architecture tomorrow. And you bring a point of view on our stack, on what we should be building, and on where AI can do something existing tooling simply can't. RESPONSIBILITIES Ship AI-powered workflows for Compute and C3 : build the agents and automations that give capacity analysts, ops leads, an

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE As a Global Capacity Manager focused on TPUs at Baseten, you will lead the "engine room" for our non-NVIDIA accelerator fleet, architecting, securing, and optimizing the Google Cloud TPU (and broader emerging accelerator) capacity that powers our customers' AI workloads. You'll own the end-to-end journey of capacity management for this fleet, from securing large-scale TPU pod allocations to building the automation that ensures reliable uptime across multi-cloud environments. This role is a great fit for entrepreneurial engineers who want to bridge the gap between high-finance asset management and deep infrastructure engineering, with a specific focus on the TPU ecosystem. You will act as the fleet orchestrator for Google's TPU architecture, ensuring Baseten never experiences a capacity outage while maintaining elite unit economics as we diversify beyond NVIDIA. To be clear, this is a high-stakes engineering role. You will be hands-on with Kubernetes orchestration while also leading specialized pods focused on the latest generation of TPU hardware, like Google's Trillium (v6e) architecture, and partnering closely with the Model Performance (MP) team to ensure workloads are tuned for TPU-specific execution. EXAMPLE INITIATIVES The TPU Frontier: Architecting the infrastructure readiness and deployment strategy for Baseten's TPU clusters, including pod slicing and topology planning Global Workload Orchestration: Bui

About the Role Together AI runs one of the largest GPU fleets in the world. The Infra Agent Systems team builds the software systems that power and automate that infrastructure. We develop production AI agents that diagnose hardware failures, investigate incidents, correlate signals across the fleet, and automate operational workflows. Alongside these agents, we build the platform they run on, including knowledge graphs, retrieval systems, orchestration frameworks, and developer tooling. You’ll work across two areas: Infrastructure Agent Systems — Build production AI agents that help operate our GPU fleet by diagnosing failures, investigating incidents, gathering evidence from live systems, and assisting with remediation. These agents are used every day by our infrastructure and datacenter teams through APIs, CLI, dashboards, and Slack. Core Agent Platform — Build the platform that powers these agents, including knowledge graphs, search and retrieval, orchestration, evaluation, and the tooling that enables agents to reason, act, and continuously improve. We’re working on something that hasn’t really been done before: building knowledge graphs and self-improving AI agents that understand, operate, and continuously improve large-scale AI infrastructure. This is an opportunity to work at the intersection of AI agents, distributed systems, infrastructure, and automation , solving challenging engineering problems with real production impact. There’s an enormous amount to build, learn, and shape as we define the future of autonomous infrastructure. responsible for delivering the software but also for operating and supporting it in production. Why this Role You’ll work on two hard problems at the same time: making AI agents trustworthy enough to operate production infrastructure, and building the knowledge, retrieval, and distributed systems that make those agents effective. You’ll have the opportunity to build foundational systems from the ground up, work on infrastructur

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We're looking for a hands-on Operations Manager to own the operational and analytical supply side of our GPU fleet. Key focus areas: GPU fleet lifecycle, health, observability, utilization monitoring, and remediation across our neocloud and bare metal environments. We contract for a fixed amount of compute capacity. GPUs drift from healthy to unhealthy over time, and this role minimizes that downtime to keep the maximum number of GPUs healthy at any given moment. This is an operator role, not people management. You'll drive execution through clear processes, metrics, reporting, vendor coordination, and cross-functional alignment.. RESPONSIBILITIES Core Responsibilities: Drive suppliers to keep the maximum amount of the GPU fleet online and healthy. Maintain a live reconciliation of contracted vs. provisioned vs. healthy vs. utilized capacity, broken out by supplier and by cluster maximizing the number of healthy GPUs. Supplier-attributed fleet health accountability: own replacement SLAs, mean time to repair (MTTR), and RMA cycle times for every in-scope supplier. SLA monitoring, credit claims, and remedy enforcement: track SLA performance against contract terms, file and pursue credit claims, and drive remediation plans when suppliers fall short. Drive internal communications where suppliers need to perform maintenance to ensure all Baseten stakeholders are aware of activities that impact availability. Scope and