Compute Country Lead Jobs

Job Details: Job Description: About the Role The CEG NAG (Networking Architecture Group) is Intel's premier team focused on defining the future of high-performance networking silicon. Our team architects next-generation networking solutions that enable hyperscale data centers, cloud infrastructure, and AI workloads to achieve unprecedented performance and efficiency. We specialize in IPU/DPU platforms, advanced packet processing architectures, and programmable networking technologies that form the backbone of modern distributed computing systems. We are seeking a Senior SoC Compute & Memory Architect to define and drive the architecture of compute complexes and high-performance memory subsystems for next-generation IPU/DPU platforms. This role is responsible for end-to-end architecture of CPU clusters, cache hierarchies, coherency models, and memory subsystems. You will optimize system-level performance, scalability, power efficiency, and programmability while ensuring seamless interaction with networking, storage, and accelerator subsystems in hyperscale environments. What You’ll Do Key responsibilities will include but not limited to: Cache Hierarchy and Coherency Architecture 1. Define and evolve multi-level cache hierarchy (private/shared caches, system-level cache). 2. Architect coherency models across compute cores, accelerators, and IO subsystems (coherent vs non-coherent interactions). 3. Evaluate tradeoffs between latency, bandwidth, scalability, and coherence domain complexity. Memory Subsystem Architecture 1. Architect system memory subsystems including: -DDR / LPDDR interfaces. -Memory controllers and scheduling policies. -Bandwidth provisioning and scaling strategies. 2. Work with Perfo

About the Team OpenAI’s Compute organization turns ambitious AI research into real-world capability by delivering the compute infrastructure behind our most advanced models. The team works across software, hardware, facilities, operations, and engineering disciplines to make enormous amounts of compute available, reliable, and efficient. As the demand for frontier AI grows, so does the complexity of the systems required to support it. Scaling this infrastructure means solving problems that cut across distributed systems, ML infrastructure, GPU fleets, power, cooling, networking, manufacturing, supply chain, and data center delivery. Our work is focused on expanding the compute foundation that enables OpenAI to train more capable models, including systems like GPT-5.6, and make frontier AI available to more people, products, and workflows. We’re looking for exceptional people across many disciplines to help build the next generation of AI infrastructure at a scale few organizations have attempted. About the Role We are hiring across a broad range of roles to help design, build, scale, and operate OpenAI’s compute infrastructure. Depending on your background, you may work on large-scale distributed systems, ML infrastructure, hardware systems, manufacturing, supply chain, data center development, or the physical engineering systems required to bring massive compute capacity online. You’ll work with teams across research, engineering, hardware, operations, and infrastructure to solve high-impact problems at extraordinary scale. This may include improving system reliability, accelerating deployment timelines, increasing operational efficiency, designing new infrastructure, or helping bring new compute platforms and facilities from concept to production. This is an opportunity to work on one of the most important infrastructure challenges in AI: building the compute foundation required to train and serve increasingly capable frontier models. Key Responsibilities Help bui

About The Role Together AI is growing its compute footprint, and making sure new capacity meets our technical standards is an important priority for the company. Every new cluster has to clear a technical bar before it carries customer workloads, and this role owns that bar. As Technical Compute Qualification Manager, you will run the process that screens and qualifies prospective compute providers, taking each prospective deployment through a structured evaluation across compute, networking, storage, power, cooling, and operations. You will coordinate various engineering partners through validation, review provider specifications and test results, and produce clear go/no-go recommendations on whether new capacity meets our standards. It is a high-impact, process-driven role for someone technical enough to know when a spec sheet does not add up, and additional diligence needs to be completed, and organized enough to drive many evaluations to closure in parallel. "You will deep-dive into critical hardware performance metrics, proactively identifying potential bottlenecks in cluster architecture before they impact our end customers training or inference workloads." Conduct diligence and work with engineering teams to make assessments regarding technical and operational resilience. Responsibilities Own and continuously improve the end-to-end qualification process for new compute capacity, from initial provider intake through final go/no-go recommendation. Run multiple provider evaluations in parallel, setting timelines, tracking status, and keeping every stakeholder aligned on what is needed and by when. Partner with infrastructure engineering, network engineering, data center engineering, and SRE teams to plan and coordinate technical validation, then translate their findings into clear decisions for leadership. Review provider technical specifications and questionnaire responses for completeness and accuracy, flagging gaps, inconsistencies, and risks that warra

Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere. The Team Our AI research team sits at the heart of our mission to unlock new dimensions of biological understanding. You will leverage state-of-the-art AI to accelerate discovery and drive transformative insights in biology—developing novel AI models purpose-built for biological research, engineering robust systems that enable breakthrough science at unprecedented scale, and translating these advances into practical tools that empower researchers worldwide. Our approach is comprehensive and integrated, bringing together world-class AI model development, exceptional engineering talent, high-quality biological data, powerful computing infrastructure, and strategic partnerships. Success requires excellence across five interconnected pillars: training frontier AI models specifically for biology; building engineering systems that maximize research velocity and efficiency; executing a sophisticated data strategy that fuels AI development; operating a world-class AI compute platform; and creating impactful products that transform AI capabilities into accessible scientific tools. The Opportunity The Security Engineering and Infrastructure team brings modern security engineering practices to the Biohub network to make sure our systems are secure while we accelerate Biomedical research. We are uniquely positioned to design, build, and scale software systems to help scientists better address the myriad challenges they face. This team works on building shared tools and platforms to be used across

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 manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, norma

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 manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, norma

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 manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, norma

About the Role Together AI is building the AI Native Cloud, an end-to-end platform for the full generative AI lifecycle, combining the fastest LLM inference engine with state-of-the-art GPU cloud infrastructure. The Together Cloud team builds the [Together GPU Clusters](https://www.together.ai/gpu-clusters) flagship IaaS product that provides high-performance, AI-ready GPU clusters through a self-serve cloud console, along with the virtualized infrastructure layer powering Together's inference, RL, and fine-tuning products. As a Staff Software Engineer focusing on AI Compute in the Together Cloud org, you will set technical direction for and build major components of the next generation AI cloud platform – a highly available, global cloud infrastructure with cutting-edge virtualization of the latest ML hardware: GB300s/VRs, BlueField DPUs, InfiniBand and dual/quad-plane RoCEv2 fabrics. That virtualized computing platform powers our own SaaS products – inference, RL, and fine-tuning – and serves external cloud customers through self-serve offerings such as on-demand/reserved Kubernetes/Slurm clusters, across dozens of data centers and hundreds of thousands of GPUs. This is an architect-and-build role. Fully automated bootstrapping of GPU data centers, high-performance virtualization of GPU compute and DC networking without compromising isolation or portability, and fault-tolerant decentralized control planes — you'll set the architecture for these across our global and in-DC services, and be a key owner of the hardest parts, in the code as well as the design. Your designs will span the IaaS layer of a greenfield Vera Rubin data center up to the global management plane that schedules capacity across all of them. At this level the job is as much leverage as code: the standards you set and the engineers you grow decide how fast the rest of Together Cloud ships. Responsibilities Own the GPU and network virtualization stack: the hypervisor, kernel, and SDN work that keeps

About the Role We are seeking a platform engineer to join our Compute team. This team owns the critical systems that power project provisioning, scaling, and the compute that our users rely on every day. You’ll work on the core platform that ensures Supabase can seamlessly scale across multiple regions, handle high-volume workloads, and provide low-latency, highly available compute services. You'll design, build, and operate systems that are both robust and highly automated — enabling us to provision new projects reliably at scale, optimize resource utilization, and deliver a seamless experience to the millions of databases which we manage. What You’ll Be Responsible For: Help build and maintain the Supabase Cloud offering; Design and implement systems to provision, scale, and manage the multi-region compute workloads powering Supabase projects; Own the full lifecycle of compute orchestration: project creation, warm pools, autoscaling, resizing, and resource optimization; Improve automation and observability across the compute stack to ensure high availability and performance; Improve our system architecture to reduce costs while balancing security, reliability, and performance; Collaborate cross-functionally with other teams to help leverage compute as a core platform offering; Participate in on-call rotations, incident response, and post-incident reviews; Resolve escalated support issues, while also identifying and resolving systemic issues across the platform. You Might Be A Good Fit If You: Have strong software engineering skills (Go and Typescript required); Experience building and operating cloud infrastructure or distributed systems at scale; Have 5+ years experience with cloud infrastructure (AWS required, additional experience with GCP and Azure is a plus) - particularly compute, networking, and storage services; Have 3+ years experience working with building custom Kubernetes controllers, and operating large-scale (tens-to-hundreds of thousands of pods) cl

Zoox made the world’s first purpose-built commercial robotaxi, combining advanced self-driving hardware and software. In order to ensure safe operation, low latency and consistent resource utilization are critical. The Planner Compute team is responsible for the performance of the largest single software component within the autonomy stack, the motion planner. The Planner Compute team is looking for an expert in GPU performance. You will instrument, monitor, analyze and optimize GPU-based algorithms that are performance-critical for our solution. The scope for GPU usage ranges from machine learning inference to custom CUDA kernels written specifically for the motion planner. In this role, you will: Analyze performance metrics to identify GPU hotspots and optimizations Contribute to the adaptation of our current planner to multiple GPU architectures with different resources Optimize ML models for latency and GPU memory usage Support engineers within Planner as a subject matter expert on CUDA & GPU performance Qualifications BS in computer science or related field and 6+ years of experience. Strong knowledge of CUDA as applied to recent GPU microarchitectures (e.g., Ampere, Blackwell) and experience debugging/optimizing GPU kernels using tools like Nsight. Strong knowledge of C++ and experience in large code bases, comfortable in Linux development environments. Experience in development, debugging, and profiling of complex multiprocess systems (e.g., robotic systems, game engines). Bonus Qualifications GPU kernel development experience There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies onl

About the Role Together AI is building the AI Native Cloud, an end-to-end platform for the full generative AI lifecycle, combining the fastest LLM inference engine with state-of-the-art GPU cloud infrastructure. The Together Cloud team builds the [Together GPU Clusters](https://www.together.ai/gpu-clusters) flagship IaaS product that provides high-performance, AI-ready GPU clusters through a self-serve cloud console, along with the virtualized infrastructure layer powering Together's inference, RL, and fine-tuning products. As a Staff Software Engineer focusing on AI Compute in the Together Cloud org, you will set technical direction for and build major components of the next generation AI cloud platform – a highly available, global cloud infrastructure with cutting-edge virtualization of the latest ML hardware: GB300s/VRs, BlueField DPUs, InfiniBand and dual/quad-plane RoCEv2 fabrics. That virtualized computing platform powers our own SaaS products – inference, RL, and fine-tuning – and serves external cloud customers through self-serve offerings such as on-demand/reserved Kubernetes/Slurm clusters, across dozens of data centers and hundreds of thousands of GPUs. This is an architect-and-build role. Fully automated bootstrapping of GPU data centers, high-performance virtualization of GPU compute and DC networking without compromising isolation or portability, and fault-tolerant decentralized control planes — you'll set the architecture for these across our global and in-DC services, and be a key owner of the hardest parts, in the code as well as the design. Your designs will span the IaaS layer of a greenfield Vera Rubin data center up to the global management plane that schedules capacity across all of them. At this level the job is as much leverage as code: the standards you set and the engineers you grow decide how fast the rest of Together Cloud ships. Responsibilities Own the GPU and network virtualization stack: the hypervisor, kernel, and SDN work that keeps

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 At Anthropic, we're building AI systems that are safe, beneficial, and transformative. Our mission is to develop AI that benefits humanity, and we believe the most powerful capabilities emerge when we thoughtfully bridge the gap between research breakthroughs and real-world applications. Frontier AI runs on datacenters, and the world is in the middle of the largest infrastructure buildout in a generation. The binding constraint on progress is increasingly everything around the chips: land, power, permits, equipment, and the design and construction of the buildings themselves. Bringing a single site online involves hundreds of people, thousands of components, and decisions that move enormous amounts of capital. Much of that work still runs on spreadsheets and handoffs. At Anthropic, we're building software and Claude-powered tools to help Anthropic scale out its compute, and we think there's an enormous opportunity to rethink how that work gets done. It's early, it's already having a real impact, and the engineers who join now will shape how one of the most consequential buildouts in the world gets done. We're looking for strong, versatile software engineers who love building for people doing real work in the physical world. You'll embed with the teams that source sites, design buildings, procure equipment, and manage construction, learn their work deeply, and build the tools and systems they rely on every day. Datacenter experience is a big plus, but what matters most is that you can earn the trust of expert operators, model a messy domain cleanly, and move fast. Responsibilities Build the core systems that track a data

Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brain of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As a NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. NVIDIA is looking for an AI Computer Engineer to join its NVIDIA Infrastructure Specialists (NVIS) team. Academic and commercial groups around the world are using NVIDIA products to revolutionize deep learning and data analytics, and to power data centers. Join the team building many of the largest and fastest AI factory systems in the world! NVIDIA is looking for someone with the ability to work on a dynamic customer-focused team that requires excellent interpersonal skills. This role will be interacting with customers, partners and internal teams, to analyse, define and implement large scale AI Factory projects. The scope of these efforts includes a combination of Networking, System Design and Automation while being the face to the customer. What you will be doing: Primary responsibilities will include deploying, managing, and maintaining AI infrastructure for new and existing customers. Be the domain expert with customers during planning calls through implementation. Handover-related documentation and perform knowledge transfers required to support customers as they begin rolling out some of the most sophisticated systems in the world! Provide feedback to internal teams such as opening bugs, documenting workarounds, and suggesting improvements. What we need to see: Bachelor’s degree in computer science, Electrical Engineering, or a related field, or equivalent experience.

About the Team Compute Foundations builds the software that manages OpenAI’s GPU compute infrastructure across sites, data centers, and infrastructure providers, supporting model training and inference. Our systems turn large, heterogeneous fleets of machines into dependable compute for research and products. We build Kubernetes-based control planes, controllers, services, and APIs that coordinate the lifecycle of machines and clusters. We connect global infrastructure management with the realities of bare-metal systems, giving clients consistent interfaces across differences in hardware, topology, and provider behavior. About the Role You will build distributed systems that provision, configure, and manage compute throughout its lifecycle. Your work will connect global services and Kubernetes controllers with the systems that bring machines online, update them safely, and recover them when something goes wrong. This role combines software architecture with an understanding of how machines and data centers work. You might design a lifecycle API, improve controller performance under high concurrency and provider rate limits, or trace a provisioning failure from an API through reconciliation to network boot or host configuration. You will help these systems remain reliable as the fleet expands across sites and generations of GPU hardware. We value depth in relevant systems and the ability to connect layers. You do not need to arrive as an expert in every component of the stack. In this role, you will: Design, build, and operate Kubernetes-based controllers and distributed services that coordinate infrastructure across sites, isolate failures, and scale as GPU capacity grows. Define APIs and resource models that let clients request and track lifecycle operations through consistent interfaces across hardware platforms and providers. Build provisioning and configuration services that coordinate network boot, hardware management interfaces, and the deployment of firmware,

About the Team The Compute & Infrastructure Strategy team handles strategy and execution of OpenAI’s compute roadmap. This team’s key responsibilities span financial analysis & reporting, capacity planning, commercial and business development, and strategic partnerships. We partner across the business to allocate and deploy our resources for the highest impact outcomes. About the Role Compute is central to OpenAI’s roadmap and vision. We are seeking a candidate to support financial and strategic work across our compute and infrastructure portfolio. This is a finance generalist role that includes core FP&A responsibilities as well as having the ability to understand investment analysis, commercial decision support, and strategic planning. You will partner closely with deal teams, technical and finance teams, Corporate FP&A and help shape decisions about how OpenAI invests in and manages compute. In this role, own and drive all financial planning, forecasting & close processes for the Compute & Infrastructure org by partnering with various stakeholders across Finance, Accounting, Tax and Engineering. You will play a critical role in planning and forecasting the company’s largest and most complex cost center ( Compute & Infrastructure ) In this role, you will: Support & scale all FP&A functions including planning, forecasting, close, budgeting, reporting, and variance analysis across compute and infrastructure Build and maintain driver based high level financial models across different elements of compute, including GPUs, CPUs, storage, networking, data centers, and power to support rapid / iterative short term and long term planning. Consolidate various investment analysis across programs and create high-quality analyses, recommendations, and Exec and Board-facing presentations Support business partners across compute infrastructure, FP&A, and strategic finance Drive the improvement of the team’s processes, tools, and ways of wo