Fleet Operations Associate Manager Jobs

About the Team: Compute Infrastructure builds the platform that turns enormous amounts of compute into a reliable engine for frontier AI. We design, provision, schedule, operate, and optimize the systems that connect accelerators, CPUs, networks, storage, data centers, orchestration software, agent infrastructure, developer tools, and observability into one coherent experience for researchers and product teams. Our work spans the entire stack: capacity planning and cluster lifecycle, bare-metal automation, distributed systems, Kubernetes and scheduling, deep system optimization, high-performance networking, storage, fleet health, reliability, workload profiling, benchmarking, and the developer experience that lets teams use enormous compute systems with confidence. At this scale, small improvements to communication, scheduling, hardware efficiency, or debugging workflows can compound into meaningful research velocity. We are hiring across Compute Infrastructure rather than for a single narrow team, and we use this opening to match strong engineers to the problems where they can have the most leverage. About the Role We are looking for engineers who want to build the compute platform behind OpenAI's research and products. You may not be the strongest in low-level systems, high-performance computing, distributed infrastructure, reliability, CaaS, agent infrastructure, developer platforms, tooling, or the user experience around infrastructure. What matters is that you can reason carefully about complex systems, write durable software, and raise the quality and velocity of the people around you. Depending on your background and interests, you might work close to hardware, close to users, on CaaS and agent infrastructure, or on the control planes and data planes in between. You could help bring new supercomputing capacity online, optimize training workloads from profiler traces and benchmarks, improve NCCL and collective communication behavior, reason about GPUs, NICs, t

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We are seeking a Strategic Finance Lead to provide finance leadership for our accelerator investments. In this role, you will be a key partner to our compute teams, providing financial expertise and guidance to optimize our compute investments and drive strategic decision-making. In this role, you’ll develop deep expertise in the economics of AI compute, from the unit economics of individual training runs to the long-range financial planning of our accelerator fleet. You’ll build financial models that inform Anthropic’s investment decisions, own planning and forecasting processes for major cost centers, and translate complex technical dynamics into clear financial narratives for leadership. This is a high-impact role for someone who thrives at the intersection of finance and technology, and who is energized by building frameworks from scratch in a fast-moving environment. Key responsibilities Finance lead for cloud accelerator compute spend, including budgeting, monthly forecasting, variance analysis, and financial plan maintenance Build and maintain detailed bottoms-up data-driven financial models for accelerator infrastructure, including long-range forecasts, cost driver analyses, and investment scenario modeling Develop deep expertise in compute contracts, pricing structures, and cost drivers, and surface optimization opportunities across our infrastructure Serve as subject matter expert for Anthropic’s overall compute capacity plan, owning source of truth and plan of record Support the implementation and management of financial planning tools (e.g., Pigment), ensuring our models and processes scale with the organiza

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic's infrastructure fleet spans a growing set of clouds, neoclouds, and on-prem sites. Every part of it is expanding rapidly. Capacity Engineering is the team that connects every stage of that growth, from capacity planning to supply management to capacity delivery to utilization. We partner on supply deals, wire telemetry from day zero, own the canonical capacity data layer, and build the planning and enforcement tools every research and product team relies on. As an Infrastructure Capacity Planner on the Demand Planning team, you'll own the medium-range demand forecast for every resource class we consume: accelerators, CPU, storage, network, and managed services. Your forecast will support sourcing and purchasing, prioritization, efficiency targets, and workload optimization. Key responsibilities Build and own the medium-range multi-resource demand forecast: accelerators by chip/interconnect class, CPU by shape, storage by tier and access pattern, egress by path, managed services by SKU — driven by model roadmap, RL/inference growth, eval volume, and retention policy rather than trend lines. Run the plan-vs-reality loop. Diff planned allocations against observed fleet occupancy weekly, surface unrecorded trades and stale allocations, and drive variance toward zero with our planning-tools and data teams. Qualify each incoming capacity tranche against the forecast before signature: right shape, region, quarter, and supporting-resource envelope (storage, egress, CPU). Partner with Finance and cost-efficiency teams to turn the forecast into core drivers covering the large majority (≥80%) of non-accelera

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic trains and serves frontier AI models on some of the largest accelerator fleets in the world, and we're now building the custom hardware systems that fleet will run on — from the package boundary out through boards, racks, interconnect, power, cooling, and the datacenter interface. We're looking for a Technical Program Manager to own execution of these hardware systems from concept through production deployment. You'll drive the program that turns architecture and specs into boards and racks that ship — managing ODMs, CMs, and component vendors on one side, and coordinating silicon, firmware, infrastructure software, supply chain, and datacenter teams on the other. This is a role on a new hardware team. You'll build the program cadence, review structure, and NPI process this team runs on. We need someone who has shipped hardware at scale, has a realistic view of ODM/CM schedules and where they break, and is comfortable owning consequential program calls without a large organization behind them. Key responsibilities Own the integrated hardware systems program plan across board, chassis, rack, and interconnect — from requirements through EVT/DVT/PVT, qualification, and production ramp — and be the source of truth on schedule, risk, and readiness. Drive execution with ODMs, contract manufacturers, and component/optics vendors: SOWs, build schedules, deliverable tracking, factory readiness, and escalation when they slip. Stand up and run the NPI cadence — build matrix, engineering reviews, DFx, issue triage, exit criteria — and keep it lightweight enough that a small, senior team actually uses it. Drive

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Responsibilities Strategize and lead productivity, yield, quality, and cost improvement engagements with global sites and the network team via plasma process optimization, new/FOAK chemistry & hardware qualification , and CIP development collaboration with dry etch suppliers. Collaborate with cross-functional teams to drive breakthroughs in defect reduction, particle/residue control, CD uniformity, profile control, and chamber-matching across the global fleet. Develop the process and hardware CIP roadmap for Dry Etch — covering chamber parts, RF/match networks, gas delivery, ESC, endpoint, and plasma source upgrades. Drive process & hardware improvement with emphasis on safety, defect reduction, cost efficiency, throughput, quality, and sustainability across Dry Etch modules ( Conductor Etch, Dielectric Etch, Poly/Gate Etch, Metal Etch, High-Aspect-Ratio (HARC) Etch, Spacer/ALE, etc.). Partner with suppliers to establish equipment start-up acceptance criteria, chamber qualification flows, and tool service requirements ; work with global facilities to address new tool installation requirements (abatement, exhaust, RF compliance, gas/chemical infrastructure). Improve tool efficiency, WPD, and utilization across all Micron Dry Etch fleets through benchmarking, MAXOUT-style productivity programs, and pathfinding solutions . Set up IoT/FDC sensors and advanced chamber telemetry for defen

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We're looking for experienced engineers to build Anthropic's cache layer as a managed service from the ground up. The Caching team is part of the Databases organization and owns the systems that keep Anthropic's hottest paths fast and correct: a managed Redis fleet, client libraries used across the company, and CDC-driven cache invalidation that solves one of distributed systems' hardest problems the right way. This is a foundational role on a small team with outsized leverage. Every millisecond you take off a hot path is multiplied across every Claude request. You'll set the technical direction for caching at Anthropic, from the data plane to the developer experience, and you'll work closely with product and research teams to make caching something engineers get for free rather than something they have to think about. Key responsibilities Drive the technical direction for caching infrastructure used across Product and Research Design, build, and operate a managed Redis fleet that scales to support millions of users across Claude's product ecosystem Build client libraries and developer-facing abstractions that make correct caching the default for Anthropic engineers Design and operate CDC-driven cache invalidation that keeps cached data consistent with source-of-truth databases Architect caching solutions that operate across GCP, AWS, first-party deployments, and other environments Optimize latency, hit rates, reliability, and cost efficiency on Anthropic's hottest paths Build observability and tooling that makes cache behavior easy to understand and debug Partner with product and research teams to understand access pat

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry’s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators. The team has a dual mandate: maximizing compute efficiency to reliably serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms. Inference systems are highly performance sensitive distributed systems. Inference serves hundreds of thousands of customers every day, and the size & span of the inference fleet requires sophisticated routing, scaling, and networking systems. Key responsibilities Design, build, and maintain the distributed systems that serve Claude to millions of users worldwide Develop resilient, flexible systems that adapt in real time to real world events Develop intelligent request routing, load balancing, and traffic management systems across thousands of accelerators Maximize compute efficiency across the fleet by autoscaling and orchestrating production, research, and experimental workloads Build and operate production-grade deployment pipelines for releasing new models to users Provide high-performance inference infrastructure that enables researcher

Senior Software Engineer - Streaming Platform Client Data streams are mission-critical at Datadog, powering near real-time communication across the vast majority of our services. Our Streaming Platform group builds the core infrastructure and abstractions that ensure Datadog remains a trusted partner for engineers worldwide. See our blog post . The Streaming Platform Client team sits at the heart of this ecosystem. We own the Rust client library (producers and consumers) with language bindings for Java, Go, and Python. We focus on building intuitive APIs and abstractions that make a powerful distributed system easy to adopt and operate for the hundreds of internal users of our library. Our library runs on critical data paths that handle hundreds of millions of messages per second making performance, observability, and reliability paramount. We also develop and operate the service that bridges the clients fleet with the platform's control plane, handling complex balancing, scaling, and static stability challenges. We are seeking a Senior Software Engineer to help us evolve these features. You will collaborate directly with our users, tackle performance-critical code, and solve complex distributed systems challenges across the control plane, client libraries, and data plane. 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: Work within a distributed, high-impact team spanning Europe and the US, building critical technologies that power data pipelines for dozens of internal teams and hundreds of services. Architect and implement resilient interactions between our client libraries and the control plane. Optimize our high-throughput, low-level streaming library to push the boundaries of performance and efficiency. Champion the developer experience by providing

Senior Software Engineer - Streaming Platform Client Data streams are mission-critical at Datadog, powering near real-time communication across the vast majority of our services. Our Streaming Platform group builds the core infrastructure and abstractions that ensure Datadog remains a trusted partner for engineers worldwide. See our blog post . The Streaming Platform Client team sits at the heart of this ecosystem. We own the Rust client library (producers and consumers) with language bindings for Java, Go, and Python. We focus on building intuitive APIs and abstractions that make a powerful distributed system easy to adopt and operate for the hundreds of internal users of our library. Our library runs on critical data paths that handle hundreds of millions of messages per second making performance, observability, and reliability paramount. We also develop and operate the service that bridges the clients fleet with the platform's control plane, handling complex balancing, scaling, and static stability challenges. We are seeking a Senior Software Engineer to help us evolve these features. You will collaborate directly with our users, tackle performance-critical code, and solve complex distributed systems challenges across the control plane, client libraries, and data plane. 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: Work within a distributed, high-impact team spanning Europe and the US, building critical technologies that power data pipelines for dozens of internal teams and hundreds of services. Architect and implement resilient interactions between our client libraries and the control plane. Optimize our high-throughput, low-level streaming library to push the boundaries of performance and efficiency. Champion the developer experience by pro

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry’s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators. The team has a dual mandate: maximizing compute efficiency to reliably serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms. Inference systems are highly performance sensitive distributed systems. Inference serves hundreds of thousands of customers every day, and the size & span of the inference fleet requires sophisticated routing, scaling, and networking systems. Key responsibilities Design, build, and maintain the distributed systems that serve Claude to millions of users worldwide Develop resilient, flexible systems that adapt in real time to real world events Develop intelligent request routing, load balancing, and traffic management systems across thousands of accelerators Maximize compute efficiency across the fleet by autoscaling and orchestrating production, research, and experimental workloads Build and operate production-grade deployment pipelines for releasing new models to users Provide high-performance inference infrastructure that enables researcher

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic's inference fleet serves Claude to millions of users across our own products and the world's largest cloud platforms. The stack that makes this possible is deep and tightly coupled: accelerator kernels, model servers, distributed routing, autoscaling, capacity management. Every layer affects the others, often in ways that are hard to see in isolation. The Inference System Dynamics team is responsible for understanding that whole system and holding it to a high bar across four dimensions: throughput, latency, reliability, and correctness . We measure how the fleet performs against its theoretical performance frontier, run cross-layer investigations to explain the gaps, and own the correctness checks that make sure Claude's outputs are right, not just fast, across hardware platforms and serving configurations. We don't own the individual components. We instrument and model them, find the highest-leverage opportunities across them, and partner with the owning teams to land the wins. You'll work across all four areas. One week that might mean tracing a tail-latency regression from request timing down through routing and batching into a kernel overhead; the next it might mean tightening a correctness eval so it catches an output regression introduced by a quantization change. We're looking for performance engineers who treat correctness as part of performance. Key Responsibilities Run cross-layer performance investigations across throughput, latency, and reliability, sizing the gap between actual fleet performance and theoretical rooflines, identifying root causes, and quantifying the value of closing them Own and

Band 6–7 Radiographer — MRI / CT Up to £50,000 · UK-based candidates only The opportunity We're partnering exclusively with a leading independent medical imaging provider to recruit motivated band 6–7 Radiographers across multiple sites in the South East. This is a brilliant opportunity for someone who wants variety, modern kit, and a genuinely supportive team culture. You'll work across MRI and CT (with the chance to develop in both), alongside occasional plain film. Settings span primary care facilities, high street locations, hospital environments, and elite sports centres — no two days look the same. What you'll be doing Delivering high-quality MRI and CT imaging across a range of dynamic clinical settings Engaging with patients, referrers, and multidisciplinary teams with professionalism and care Working on a 7-day rota covering a mix of shifts — flexibility is key Contributing to a growing imaging fleet including exciting new state-of-the-art MRI and CT installations What we're looking for Band 6 or 7 Radiographer with HCPC registration Based in the UK (essential for consideration) Full UK driving licence and access to a vehicle (essential — multi-site role) Experience in a fast-paced medical environment is desirable but not essential Salary & benefits Salary Up to £50,000 Annual leave Up to 35 days Other benefits Pension · Healthcare · Parking Salary is dependent on experience. Pension scheme, private health care plan, and free on-site parking included. Interested? Apply via the link below or reach out directly. We'd love to hear from you — send your CV along with a brief note about your modality experience and availability. Pulse AHP acts as an employment agency for permanent roles. Candidates must be based in the UK and hold a valid HCPC registration to be considered. This vacancy is being recruited on behalf of our client — full details will be shared upon application.

The Public Sector software engineers (SWEs) create the core product building blocks forward-deployed teams use to develop agentic capabilities that function across multiple domains. SWEs responsibilities include building the systems required to ingest and process federal datasets to support real-time decision-making in contested environments. We develop novel agentic enabling capabilities that includes: Create multi-layered guardrails around agents Optimize data retrieval for agents Orchestrate fleets of asynchronous agents Automatically alerts users to deviations in data Illustrating how an agent reached a decision As a Staff Software Engineer, you will orchestrate the implementation of vertical features and horizontal capabilities to include mentoring other engineers on defining requirements with stakeholders and communication tradeoffs of technical implementations on feature and capabilities until they are accepted by the stakeholders. You will: Orchestrate feature implementation across the Federal engineering team to ensure architectural consistency. Define technical strategy for agentic guardrails, explainability, and fleet orchestration. Ensure system reliability and performance across multiple security classifications and network types. Mentor engineers in the process of defining requirements with stakeholders and gathering acceptance. Communicate high-level technical trade-offs and implementation strategies to senior government stakeholders and Scale C-Suite members. Influence the long-term product strategy and technical roadmap for the Federal business unit. Consult on the architecture of AI-powered solutions for large-scale federal contracts. Ideally you will have: Full Stack Development: Proficiency in front-end, back-end development and infrastructure, including experience with modern web development frameworks, programming languages, and databases Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in

About the Team Our DD Labs team builds real-time autonomous delivery systems. The Planning & Decision-Making group is investing heavily in deep reinforcement learning to move beyond classical planning, learning policies that generalize across novel driving scenarios, handle long-tail edge cases, and improve continuously from large-scale fleet data. Our models jointly handle prediction and planning in a single unified architecture. Our stack is pure JAX end-to-end: the same code you train with is the code that runs on the robot. No C++ rewrites, no TensorRT export. A new policy goes from training to on-vehicle deployment in minutes. About the Role As a Senior/Staff Deep RL Engineer, you will design, train, and deploy deep reinforcement learning policies that make real-time driving decisions for our autonomous vehicles. You will own the full lifecycle, from problem formulation and reward design through large-scale distributed training to on-vehicle inference. You'll help define how learned components compose with the rest of the autonomy stack to produce robust, shippable behavior. You’re excited about this opportunity because you will… Formulate complex driving tasks as RL problems with well-shaped reward functions and expressive state/action representations. Design and train model-based deep RL agents using GPU-accelerated simulation at massive scale, including improving the simulator itself. Build and maintain distributed training infrastructure in JAX across large compute clusters. Build agentic optimization systems that automatically improve code, run experiments, analyze metrics, and iterate on RL policies with minimal human intervention. We’re excited about you because… BS/MS/PhD in CS, EE, Robotics, or a related field, with a strong foundation in reinforcement learning and deep learning. You have proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing,

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry's largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators. The team has a dual mandate: maximizing compute efficiency to serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms. As a Staff Software Engineer on our Inference team , you will work end to end, identifying and addressing key infrastructure blockers to serve Claude to millions of users while enabling breakthrough AI research. Strong candidates should have familiarity with performance optimization, distributed systems, large-scale service orchestration, and intelligent request routing. Familiarity with LLM inference optimization, batching strategies, and multi-accelerator deployments is highly encouraged but not strictly necessary. Strong candidates may also have experience with: High-performance, large-scale distributed systems Implementing and deploying machine learning systems at scale Load balancing, request routing, or traffic management systems LLM inference optimization, batching, and caching strategies Kubernetes and cloud infrastructure (AWS, GCP) Pytho