Cluster Hr Head in United States

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? The Data Infrastructure team at Cohere is responsible for the storage and data movement layer underlying every model training run. We're building the unified storage layer that feeds our training workloads. It needs to serve petabytes of training data and model checkpoints fast enough to keep thousands of GPUs busy across several training clusters. In this role, you’d have an opportunity to build this system from the ground up. You’d be a key contributor, working on a problem few teams have had to solve at this scale. In this role, you will: Design, build, and operate the distributed storage system that feeds model training and evaluation. Run this system multiple on Kubernetes clusters at petabyte scale. Work with researchers and training-infra teams on how jobs actually read and write data, and turn that into throughput, latency, and durability requirements Work through the networking, I/O, and consistency problems of moving large datasets and checkpoints across regions and backends, with GPU idle time and time-to-insight as the measures of success You may be a good fit if you have: Strong storage fundamentals,

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role You will build the model runtime within the inference engine that executes complex, frontier models at scale on OpenAI’s custom silicon. The runtime will sit between models running on the hardware and the upper layers of the cluster serving software stack, translating demanding inference workloads into efficient execution while optimizing for throughput, latency, utilization, and reliability. You will work across model architecture, distributed systems, compilers, kernels, and silicon to design a production-grade runtime comparable in ambition to systems such as vLLM and SGLang, but customized and optimized for OpenAI’s AI accelerator. Your work will shape how new model capabilities map onto the platform and how quickly custom silicon can deliver meaningful performance in production. In this role, you will: Design and implement the LLM inference runtime for frontier models running on custom silicon. Build scheduling, continuous batching, memory management, KV-cache management, and execution orchestration for high-performance inference. Develop distributed execution strategies across chips, hosts, and racks, including model partitioning, communication, and synchronization. Optimize end-to-end latency, throughput, memory efficiency, and hardware utilization across diverse model architectures and serving workloads. Partner with kernel, compiler, architecture, and silicon teams to co-design interfaces and remove performance bottlenecks across the stack. Enable new

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. As our TT-Distributed Software Engineer, you will develop and optimize distributed software systems that power the most efficient and highest-performing AI and HPC clusters. In this role, you'll work on distributed programming across multiple nodes, utilizing systems programming, inter-node communication, and Tenstorrent’s scalable architectures to advance the state-of-the-art distributed inference and training infrastructure. This role is hybrid, based out of Santa Clara, CA; Austin, TX; or Toronto, ON. 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 Strong C or C++ engineer with solid foundations in systems programming, operating systems, and distributed systems principles. Enthusiastic about distributed computing, including IPC, socket programming, and cluster resource coordination. Comfortable reasoning about scalability, fault tolerance, and performance across multi-node environments. Curious and first-principles thinker who challenges conventional approaches to distributed system design. Motivated to grow into a deep technical expert in large-scale distributed AI infrastructure. What We Need Architect, implement, and optim

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

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 hiring a Product Data Scientist to establish how product decisions at Baseten are made with data. You'll work directly with Product and Engineering, alongside GTM to determine measurement, strategy, experimentation and implementation. This is a foundational, hands-on role. You'll define what success looks like across a technical, usage-based platform and turn ambiguous questions into analyses, forecasts, and experiments that shape product strategy. You'll work from clickstream and product events through inference telemetry and observability data, helping Baseten make faster decisions about reliability, performance, adoption and developer experience. RESPONSIBILITIES Partner directly with Product and Engineering: frame the questions that matter, define success criteria, and turn analysis into roadmap, launch, and prioritization decisions. Define how product success is measured: establish metrics across activation, adoption, retention, expansion, reliability and user experience. Support experimentation and launches: design measurement plans, analyze A/B experiments and controlled rollouts, and translate results into product decisions. Diagnose reliability and scaling behavior: join customer signals with request, replica, deployment, and cluster telemetry to find patterns in release bottlenecks, unhealthy replicas, and models without traffic. Define the enterprise customer journey and measure feature adoption

About the Team The Core Services organization builds and runs the mission-critical online services that product teams rely on in production. We own foundational distributed systems and platform capabilities that enable reliable execution, high-performance services, and large-scale file/data needs across our products. This team is distinct from developer infrastructure and data infrastructure—our focus is production service foundations and core runtime services. About the Role We’re hiring an Engineering Manager, Core Services to help lead teams responsible for highly reliable, high-scale distributed systems that sit on the critical path for OpenAI products. Your team will own foundational production systems that OpenAI’s product engineering teams build on. You’ll collaborate closely with product and infrastructure partners to ship reliable services quickly, and help scale systems and teams as OpenAI grows. You’ll partner closely with senior engineering leaders to scale the org, mature operations, and drive major platform initiatives. This role requires strong technical ability. You’ll be responsible for: Managing and growing a high-performing team of infrastructure engineers. Leading teams building and operating large, critical production platforms, including cluster reliability, scaling, and rollout safety. Building and operating mission-critical distributed systems with strong operational rigor (SLOs, incident response, capacity planning, reliability). Setting technical direction for platform foundations such as workflow/orchestration capabilities, large-scale file/blob/storage services, and core service foundations. Partnering with a broad set of stakeholders, including product engineering, adjacent infrastructure teams, and (where relevant) finance/cost partners. Coaching, mentoring, and developing engineers and emerging leaders. You might thrive in this role if you: Have significant experience leading teams that run mission-critical infrastructure in production

About the Team OpenAI's Industrial Compute organization builds and operates the infrastructure required to train and serve frontier AI models. The Capacity Planning team connects rapidly changing research and product demand with the compute, networking, storage, power, data center, hardware, and operational resources required to make that demand executable. About the Role We are seeking a Technical Program Manager to build and lead capacity planning across OpenAI's large-scale AI infrastructure. You will translate uncertain workload demand into clear infrastructure requirements, allocation decisions, supply commitments, activation priorities, and long-range capacity strategies. This role sits at the intersection of research, engineering, infrastructure, finance, sourcing, deployment, and operations. You will create the planning models, operating cadences, governance mechanisms, and source-of-truth systems that allow teams to understand what capacity is required, what is available, what is at risk, and what decisions must be made. This is not a finance-only forecasting or reporting role. Success requires technical fluency across the infrastructure stack, strong analytical judgment, and the ability to move consequential decisions forward when requirements, timelines, and supply conditions change quickly. Key Responsibilities Own capacity-planning processes across near-term workload allocation, quarterly execution, and longer-range infrastructure horizons. Translate research, training, inference, and product demand into compute, accelerator, cluster, networking, storage, rack, power, and site requirements. Develop scenarios that make assumptions, confidence levels, constraints, sensitivities, and decision points explicit. Reconcile requested demand against contracted, delivered, installed, activated, and workload-usable capacity. Partner with research and engineering teams to understand workload priorities, technical dependencies, utilization patterns, and changing req

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 building the world’s fastest, most efficient AI compute clusters. TT-Fabric is the high-performance nervous system of this platform: the low-level networking layer that lets thousands of RISC-V and AI processors snap together into a single, massively parallel distributed supercomputer. If you love squeezing nanoseconds out of hot paths, designing protocols that move data at absurd scale, and turning messy hardware constraints into elegant distributed systems, this is an opportunity to shape the fabric that future AI models will run on This role is hybrid based out of Santa Clara, CA; Austin, TX; or Toronto, ON. 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 We Are Strong systems engineer with deep C or C++ experience and comfort working in low-level or bare-metal environments. Passionate about hardware-software interaction, performance tuning, and eliminating inefficiencies at the protocol level. Curious about networking, synchronization, and communication across large clusters. Comfortable reasoning from first principles and challenging industry conventions. Motivated by building infrastructure that directly impacts large-scale

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 us Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone. Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation. Job Summary We are seeking an experienced Principal Hardware Diagnostics Engineer to design and develop diagnostics software used to monitor hardware health and diagnose system-level issues across Graphcore’s AI infrastructure platforms. This role focuses on building diagnostics agents, tools, and analytics frameworks that enable engineers and automation systems to identify, isolate, and resolve hardware issues across blade-level servers and rack-scale clusters. The Team Graphcore is a globally recognised leader in Artificial Intelligence computing systems. The company designs advanced semiconductors and data centre hardware that provide the specialised processing power needed to drive AI innovation, while delivering the efficiency required to support its broader adoption. The Systems Engineering and Platform Validation team ensures Graphcore’s AI compute platforms are reliable, diagnosable, and operationally robust at scale. The team co

About us Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone. Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation. Job Summary We are seeking a Senior Principal Network Engineer to help design, deploy, and optimize next‑generation AI data center networks. AI training and inference workloads require extremely high bandwidth, deterministic low latency, and zero‑packet‑loss networking environments. In this role, you will partner closely with the Network Architecture Lead to design and scale high‑performance computing (HPC) network fabrics supporting GPU clusters. You will work across hardware, networking, and AI application layers to ensure Graphcore’s large‑scale AI infrastructure operates at peak performance. The ideal candidate brings deep experience operating hyperscale or HPC data center networks and has expertise in high‑speed Ethernet fabrics, RDMA technologies, advanced automation, and telemetry systems. The Team The Data Center Network Engineering team designs and operates the high‑performance network fabrics that power Graphcore’s AI compute platforms. The team collaborates closely with hardware engineering, AI researchers, and infrastructure teams to build scalable networking environments optimized for distributed training and infe

🚀 About WRITER WRITER is where the world's leading enterprises orchestrate AI-powered work. Our vision is to expand human capacity through superintelligence. And we're proving it's possible – through powerful, trustworthy AI that unites IT and business teams together to unlock enterprise-wide transformation. With WRITER's end-to-end platform, hundreds of companies like Mars, Marriott, Uber, and Vanguard are building and deploying AI agents that are grounded in their company's data and fueled by WRITER's enterprise-grade LLMs. Valued at $1.9B and backed by industry-leading investors including Premji Invest, Radical Ventures, and ICONIQ Growth, WRITER is rapidly cementing its position as the leader in enterprise generative AI. Founded in 2020 with office hubs in San Francisco, New York City, Seattle, Austin, Chicago, and London, our team thinks big and moves fast, and we're looking for smart, hardworking builders and scalers to join us on our journey to create a better future of work with AI. 📐 About the role Join WRITER's security team as a staff detection and response engineer and help protect the AI infrastructure that's transforming how the world works. You'll build sophisticated detection systems that identify attacks targeting our AI platform, training data, and model deployments while creating automated response capabilities that scale with our explosive growth. This isn't just traditional security work – you're defending cutting-edge AI/AGI systems against adversaries who are evolving their tactics as fast as AI itself advances. This role combines hands-on security engineering with strategic thinking to stay ahead of novel threats that don't exist in textbooks yet. You'll be the operational arm of our security function, translating threat intelligence into real-time detections, coordinating incident response across multiple teams, and hunting for sophisticated attacks across GPU clusters and distributed training environments. If you're excited by the challen

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 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 Lead at Baseten, you will lead the "engine room" of the company, architecting, securing, and optimizing the global GPU fleet that powers our customers' AI workloads. You’ll own the end-to-end journey of capacity management, from securing multi-million dollar GPU clusters to building the automation that ensures 99.9% 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. You will act as the fleet orchestrator for the world's most advanced chips, ensuring Baseten never experiences a capacity outage while maintaining elite unit economics. 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 next generation of hardware, like NVIDIA’s Blackwell (B200) architecture. EXAMPLE INITIATIVES The B200 Frontier: Architecting the infrastructure readiness and deployment strategy for Baseten's first Blackwell GPU clusters. Global Workload Orchestration: Building "Multi-cloud Capacity Management" systems to move customer workloads seamlessly across regions to optimize cost and latency. Precision GPU Triage: Developing automated Go-based operators to identify, cordon, and repair unhealthy H100 nodes in under an hour. The Supply Chain of Intelligence: Partnering with lead

Business Systems drives efficiency across Datadog through business process analysis, systems automation and integrations, AI agent and MCP development, and vendor/software review. The team is increasingly embedded in cross-functional initiatives across People, Finance, GTM , Legal, Recruiting, and Technical Solutions — translating ambiguous business problems into scoped, buildable solutions and owning delivery end-to-end. This is not a generalist BSA role. Each Senior BSA will own a cluster of business functions end-to-end, acting as an internal product manager for their domain rather than processing inbound requests reactively. There are two openings, each covering a different domain: People, Recruiting, Legal or Finance, GTM, Procurement As the role evolves alongside AI, Senior BSAs leverage agentic tools for data aggregation and context gathering while remaining the critical human-in-the-loop layer — owning business context and product outlook, validating use cases, managing stakeholder relationships, and making the judgment calls agents can't. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Discovery and scoping: Translate ambiguous asks from business stakeholders into well-defined requirements that Business Systems Engineers can build against. Many stakeholders don't know what they want or the cross-functional impact of what they're asking for — you surface both before engineering begins. Cross-functional visibility: Identify dependencies, downstream impacts, and integration considerations that requesting teams miss. Proactive opportunity identification: Develop deep domain knowledge to identify automation and AI opportunities before they become inbound requests, shifting the team from reactive in