Compute Country Lead Jobs

About the Team: OpenAI, in close collaboration with our capital partners, is embarking on a journey to build the world’s most advanced AI infrastructure ecosystem. Our Stargate program develops and deploys massive, state-of-the-art data center campuses in partnership with industry leaders today—and through future OpenAI infrastructure projects tomorrow. We design for scale, speed, and reliability, and we need experienced technicians who can translate network blueprints into physical reality. About the Role: We are seeking a Senior Data Center Networking Technician who thrives in fast-moving build environments and is eager to roll up their sleeves during active datacenter deployments. Your first assignment will focus on the physical bring-up of network infrastructure at a large partner-operated campus, collaborating with partner teams and their delivery vendors to achieve agreed performance and reliability targets. As that campus reaches steady state, you will transition to lead network deployment for future OpenAI data center projects, defining standards and guiding implementation across multiple locations. Candidates must be able to sit onsite in Abilene, Texas 5 days per week Key Responsibilities Serve as OpenAI’s technical lead technician during the current campus build, partnering with internal engineers and external contractors on design reviews, installation plans, and acceptance criteria. Spend significant time on the data-center floor performing inspections, assisting with cable routing/termination when needed, conducting fiber testing (OTDR, power levels, continuity), and resolving installation challenges in real time. Troubleshoot and optimize cabling routes, patching, and equipment turn-up to ensure clean, reliable handoff to network operations. Contribute to design discussions and peer reviews for structured cabling and physical network layouts, providing practical field feedback to engineering teams. Develop repeatable engineering standards, as-built do

Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. About the role: We are seeking talented distributed systems engineers who are passionate about building innovative solutions for application deployment. Your mission will be to enhance the capabilities of Replit Infrastructure, optimize performance across global regions, and drive efficiency while delivering an exceptional user experience. If you have a strong foundation in software development, a deep understanding of cloud technologies, and a track record of delivering high-quality code, we want to hear from you. In this role you will: Expand Replit's cloud infrastructure offerings: Launch new cloud products to be used by Replit Agent to build complex apps. Collaborate with cross-functional teams to design and implement these features, empowering developers with a comprehensive suite of tools to build and deploy their applications efficiently. Enhance reliability and scalability: Identify bottlenecks, optimize critical paths, and implement robust monitoring and alerting systems. Work closely with the SRE team to ensure high availability and minimal downtime. Enable our customers to seamlessly scale their applications to meet the demands of their growing user base. Improve utilization of cloud infrastructure: Analyze our infrastructure costs and identify opportunities for optimization. Implement strategies to reduce cloud expenses without compromising performance or reliability. This could involve techniques such as resource provisioning, auto-scaling, cost-aware scheduling, and data lifecycle management. Your efforts will directly contribute to the financial efficiency of our cloud services. Required skills and experience: Distributed systems: Track record of working with platform-as-a-service, distributed storage, o

About the Team The Storage Infrastructure team builds and operates the storage foundation behind OpenAI’s most demanding workloads. We work directly with research to design storage systems for rapidly evolving experiments, while also powering production at scale. We own the platform end to end: backend systems, user-facing services and APIs, and the control planes that manage how data is placed, moved, and retained over time. Our stack spans cloud and in-house object stores across very different workload profiles, from GPU-attached systems to dedicated storage hardware. We also build the federation layer that unifies these backends behind a simple interface and routes each workload to the right storage solution. About the Role You will help build the storage platform that powers OpenAI’s research and production systems. This is a hands-on infrastructure role for engineers who want to work on deeply technical systems at scale and own them in production. You’ll work across object storage, cross-region data movement, lifecycle management, and the federation layer that provides a unified interface across multiple backends. Much of our stack runs on Kubernetes, and we primarily build services in Rust. In this role, you will: Build and operate storage services that underpin OpenAI’s research infrastructure Develop object storage systems across cloud and in-house environments Build systems for cross-region data movement, replication, and recovery Design lifecycle management capabilities that keep data durable, available, and cost-effective Evolve the federation layer that unifies multiple backend systems behind a simple interface Improve performance, reliability, and operational excellence across the platform Collaborate closely with researchers and infrastructure teams to support rapidly evolving workloads You might thrive in this role if you: Have experience building or operating distributed systems in production Have worked on storage infrastructure, object stores, dist

Sr Engineer, DPG Compute PE Y/Q — Boise, ID - Main Site. Apply via Workday.

Engineer, DEMQRA Compute PQRE — Penang, Malaysia - Grande. Apply via Workday.

Data Science, AI Compute Strategy, Canada — 20 Locations. Apply via Workday.

Want to help us chart a course toward the future of the data center? We are seeking a world-class Technical Product Manager to define and build the next generation of NVIDIA's Accelerated Computing platforms. If you are passionate about technology products, have experience with data center technology and servers, and have been looking for a role where you'll be able to stretch your system design, project management, and creative marketing muscles on the same day, this high visibility role offers an outstanding opportunity. What you will be doing: Working at the forefront of accelerator technology, and collaborating with our hardworking, collaborative and diverse team, at the cutting-edge intersection of hardware and software. Lead our Hardware SKUs and Help Define our Platforms Maintain specifications, features, and schedules for hardware SKUs while working with hardware partners on and end customers their resulting paths into the market. Own the definition, delivery, enablement, and scheduling of all the bits and bytes-- from modules and chips, to reference board samples and developer kits-- that let people build on NVIDIA. Partner with our platform team to drive system concepts to execution, including defining support for peripherals, GPUs, NICs, memory, cooling, and more. Develop delivery schedules, RMA policies, and other tightly coordinated plans critical to enabling a thriving hardware and partner ecosystem. Get The Right Things Built! Be our representative for the needs of a diverse range of customers from Enterprise and HPC to Cloud, through all phases of the platform lifecycle: requirements to production. Collaborate with various business units (Server, Networking, GPU) as well as end customers and OEMs to lead Plans of Record for platform delivery and associated collateral, feature requirements, compliance, and certification

Senior Software Engineer, At Scale Compute Analysis — 5 Locations. Apply via Workday.

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. Micron’s DRAM Product Architecture Group partners across fabs, test, module, and system teams worldwide to deliver high-performance memory products. We work across geographies and functions to solve complex technical challenges, drive product innovation, and enable industry-leading DRAM solutions. Our team values collaboration, continuous learning, and making a measurable impact through engineering excellence. As a New College Graduate DRAM Yield & Quality Engineer, you will play a key role in monitoring and improving the health of Micron’s server DRAM products. Working with experienced engineers and cross-functional teams around the globe, you will analyze product and manufacturing data, investigate technical issues, and contribute to strategies that improve yield, quality, and performance throughout the product lifecycle. This is a highly visible role with direct impact on customer success and product reliability. Responsibilities Analyze silicon parametric, test, and manufacturing data to identify trends, resolve product issues, and support root cause investigations. Contribute to yield and quality improvement initiatives across new product introduction (NPI), qualification, ramp, and high-volume manufacturing. Collaborate with fab, test, module, quality, and operations teams to drive execution of product health objectives. Develop and maintain automation solutions to improve engineering efficiency, data analysis, and reporting. Apply AI, Agentic AI, and Generative AI tools to en

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 As part of the Anthropic security department, the compliance team owns understanding security and AI safety expectations, as established by regulators, customers and (nascent) industry norms (which we also seek to influence). The compliance team uses this understanding to provide direction to internal partners on the priorities of security and safety requirements they must meet. The compliance team assures regulators and customers that those expectations are met by earning security credentials and responding to direct inquiry about Anthropics security program from auditors, customers and partners. This opportunity is unique, as we work to secure today’s most novel and valuable asset types, we must build a new kind of compliance program, assuring the safety of artificial intelligence capabilities. Key responsibilities Understand how Anthropic's security and privacy capabilities across major cloud platforms implement common frameworks (e.g., NIST 800-53, NIST 800-171, ISO 27001, ISO 27701, CSA CCM, and SOC 2). Build scalable data center and hyperscaler processes and documentation systems that will support future expansion to additional geographies and compliance frameworks. Assess and mitigate security and operational risks, implementing corrective actions, and managing vendor compliance. Coordinate engagements with independent assessors to earn security and privacy certifications and attestations important to Anthropic customers and enterprise partnerships, and to meet regulatory obligations. Write, update and enact policies capturing security, privacy, and AI safety requirements. Maintain and enhance Anthropic's s

About the Role REMOTE IN INDIA We're looking for a software engineer to build the Kubernetes-native control plane that provisions and runs our GPU inference fleet. You'll design a manifest-driven API where the inference team declares what they need, whether that's a cluster, a model deployment, or a capacity change, and our controllers handle the reconciliation, provider/runtime selection, and lifecycle management underneath, so the inference team never has to know or care which specific serving stack, scheduler, or hardware pool is doing the work. You'll also build the systems that keep the fleet efficient, not just running, including defragmentation and rebalancing logic that consolidates scattered workloads back into contiguous capacity, and scheduling/bin-packing improvements that push GPU utilization up without hurting latency. The core value we're after is decoupling the people building on top of the platform from the operational and runtime complexity underneath, while squeezing more usable capacity out of the same hardware. You'll build the controllers, reconciliation loops, and self-service surface (API/CLI, not tickets) that make that decoupling real, plus the event-driven health, remediation, and utilization systems that keep it running and efficient without a human in the loop. Strong candidates have hands-on experience with Kubernetes controller/CRD patterns, have built or operated a platform API that abstracts multiple backends behind one interface, understand GPU scheduling and capacity efficiency (fragmentation, bin-packing, right-sizing), and think about GPU infrastructure as software to be engineered. A product mindset - you've built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship. You build it, you own it. You are not only responsible for delivering the software but also for operating and supporting it in production. Responsibilities Build the provisioning state machine

About the Role We're looking for a Software Engineer to build the systems that treat infrastructure as software. This role owns the software state machines that provision hardware, bring it into service, and manage its full lifecycle — turning racks of GPUs into running inference clusters without a human touching a runbook. The Research and Inference team is your customer: today they file tickets and wait; the target state is that they issue a single API call to stand up, scale, or tear down a cluster, and the system takes care of the rest. The platform is manifest-driven such that teams declare the desired state of a cluster or host — shape, topology, software stack — and the system is responsible for reconciling reality to that manifest, continuously, through every stage of its lifecycle. You will design the engines that manifest the schema, the engines that execute against it, and the workflows that carry a piece of hardware or a cluster from one state to the next—taking it from bare metal to a fully functioning AI cluster for training or inference. You'll write production code which is typed, tested, versioned, and deployed through CI/CD that models infrastructure state and reconciles it, the same way a Kubernetes controller reconciles a cluster's desired state. Success looks like eliminating manual provisioning work, not documenting it better. A product mindset - you've built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship. You build it, you own it. You are not only responsible for delivering the software but also for operating and supporting it in production. Responsibilities Build the provisioning state machine: design and implement the software that models the full lifecycle of a physical host from discovery, inference bring-up to GPU driver/CUDA stack, health validation, and decommission/RMA — as explicit, versioned states and transitions. Build the self-service API: design declarative

About the Team OpenAI, in close collaboration with our capital partners, is embarking on a journey to build the world’s most advanced AI infrastructure ecosystem. This team is central to this mission, setting the core infra strategy and implementing this vision. From site selection to the buildout process, this team sits at the intersection of commercial, technical, strategy, and operations, interacting with teams and executives inside and outside of OpenAI. About the Role We are seeking experienced Data Center Mechanical and Electrical/Power Design Engineers with expertise in designing, operating, and maintaining large-scale data center campuses. The ideal candidate for this role will have extensive background and experience in design and managing critical equipment and facilities, design and operation of MEP (Mechanical, Electrical, Plumbing) systems, and overseeing operational activities from initial phases of Data Center build through delivery and ongoing maintenance. The ideal candidate will have a strong technical background, operational leadership experience, and a proven ability to collaborate with external vendors on critical infrastructure. This role offers the opportunity to lead transformative data center projects with high visibility and impact. If you are passionate about delivering cutting-edge infrastructure solutions, we encourage you to apply. Key Responsibilities Oversee building and MEP design, operation, and maintenance, including reviewing building and MEP drawings and proposals across all project phases. Lead operational activities for large-scale data center campuses, from early design phases through delivery and daily operation. Operate and maintain critical data center facilities and equipment, ensuring reliability and performance. Collaborate with external vendors to select, procure, and manage critical equipment, such as generators, UPS, chillers, and CDUs. Provide technical expertise on all aspects of data center building, equipment, and

Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. Tenstorrent is seeking an experienced CPU Architect/Micro-Architect to drive the next generation of high-performance RISC-V CPU Vector (RVV), Floating Point, and Matrix extensions. In this role, you will drive both the architecture and micro-architecture of compute execution units and subsystems that enable the efficient execution of modern high-performance computing (HPC), and AI inference/training workloads. Working closely with architects, software teams, silicon designers, and external IP partners, you will translate HPC/AI workload requirements into scalable CPU architectures that optimize performance, power, and area (PPA) while enabling future HPC/AI computing platforms. This role is ideal for an architect/micro-architect who enjoys working across hardware and software boundaries, defining next-generation compute architectures, and shaping how CPUs and HPC/AI workloads work together. This role is remote, based out of North America or Spain. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who You Are Passionate about designing next-generation compute architectures for AI, HPC, and large-scale datacenter systems. Experienced in CPU

About the Role We're looking for a software engineer to build the Kubernetes-native control plane that provisions and runs our GPU inference fleet. You'll design a manifest-driven API where the inference team declares what they need, whether that's a cluster, a model deployment, or a capacity change, and our controllers handle the reconciliation, provider/runtime selection, and lifecycle management underneath, so the inference team never has to know or care which specific serving stack, scheduler, or hardware pool is doing the work. You'll also build the systems that keep the fleet efficient, not just running, including defragmentation and rebalancing logic that consolidates scattered workloads back into contiguous capacity, and scheduling/bin-packing improvements that push GPU utilization up without hurting latency. The core value we're after is decoupling the people building on top of the platform from the operational and runtime complexity underneath, while squeezing more usable capacity out of the same hardware. You'll build the controllers, reconciliation loops, and self-service surface (API/CLI, not tickets) that make that decoupling real, plus the event-driven health, remediation, and utilization systems that keep it running and efficient without a human in the loop. Strong candidates have hands-on experience with Kubernetes controller/CRD patterns, have built or operated a platform API that abstracts multiple backends behind one interface, understand GPU scheduling and capacity efficiency (fragmentation, bin-packing, right-sizing), and think about GPU infrastructure as software to be engineered. A product mindset - you've built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship. You build it, you own it. You are not only responsible for delivering the software but also for operating and supporting it in production. Responsibilities Build the provisioning state machine: design and implement