Ai Infrastructure Systems Engineer Jobs

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 Global Sourcing Director to lead supply and demand planning across Graphcore’s global operations. This role will sit at the intersection of Finance, Sales, Marketing, Engineering, and Operations to translate market demand into actionable supply chain strategies. You will drive forecasting, sourcing, inventory optimization, and operational scalability to support the rapid growth of next-generation AI infrastructure platforms. The Team The Supply Chain and Operations team is responsible for enabling Graphcore’s global product delivery and operational excellence. The team collaborates cross-functionally to manage sourcing, forecasting, inventory, and supplier engagement to ensure reliable execution across the business. Responsibilities and Duties Develop and execute end-to-end demand and supply plans across global product lines and inventory channels. Own the Sales & Operations Planning (S&OP) process and align supply plans with financial and business objectives. Collaborate with Finance, Sales, Marketing, and Engineering teams to support operational and product planning. Identify supply risks

Sr Software Engineer - Distributed Systems Engineer, EDA Infrastructure NVIDIA is hiring engineers to build and scale the infrastructure that supports our Electronic Design Automation (EDA) workloads. We are looking for engineers with strong programming skills, a deep understanding of distributed systems, experience operating large-scale production infrastructure, and excellent communication and planning abilities. You will help design reliable automation and platform services that manage large fleets of GPU-based and CPU-based compute systems used by engineering teams across NVIDIA. The ideal candidate is comfortable working across software, operating systems, cluster schedulers, networking, storage, and physical hardware. You should enjoy solving complex operational problems, eliminating repetitive work through automation, and building systems that remain reliable as infrastructure grows. If you are creative, pragmatic, and motivated to improve how critical engineering workloads are delivered, we would like to hear from you. What You Will Be Doing: Design and build platforms that automate the provisioning, configuration, operation, and lifecycle management of large-scale GPU and CPU compute infrastructure. Develop monitoring, health-management, and remediation systems that improve the reliability, availability, and utilization of EDA compute environments. Automate hardware deployment, operating-system configuration, firmware and software updates, cluster enrollment, and recovery workflows. Build reliable services and workflows that integrate with workload schedulers, infrastructure management systems, and observability platforms. Use hardware diagnostics, operating-system signals, scheduler data, and network and storage telemetry to identify

It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 50+ million monthly active customers. We want to redefine the world’s relationship with money to make it more relatable, instantly available, and universally accessible. Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We’ve been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy. The Role We build AI products, and the quality of our evaluations sets the ceiling for how good those products can be. The speed of our evaluations determines how quickly we can improve them. We are looking for an engineer to build the infrastructure and tooling that make high-quality AI evaluation possible at Block's scale. You will help teams understand whether a model or product change is actually better, whether a result is statistically meaningful, and whether offline evaluation is predicting what happens with real users. Our evaluation approach combines offline evals that encode our definition of a good response, online evals that show how people actually respond, and a feedback loop that keeps the two converging. Your work will turn that approach into systems that product teams can use quickly, reliably, and with confidence. This is a high-impact, early-stage area with broad surface area. You will help decide what to build first, then build the platform that helps teams ship better AI products faster. You Will Build an execution engine that can score candidate versions

About the Role Lead a team of AI Infra (Systems Engineers) at Together based out of our office in Amsterdam, you and the SRE team are responsible for keeping all user-facing services and production systems running smoothly. You are a blend of a pragmatic operator and a software engineer that applies sound engineering principles, operational discipline, and mature automation to our operating environments and codebase. You specialize in systems (operating systems, storage subsystems, networking), while implementing best practices for availability, reliability and scalability, with varied interests in algorithms and distributed systems. Responsibilities Be on an on-call (PagerDuty) rotation to respond to incidents that impact availability Manage, develop and coach the SRE Team. Build and run our infrastructure with Ansible, Terraform, and Kubernetes to enable scaling to a massive number of concurrent users Build monitoring systems to ensure the highest quality service for our customers Design and implement operational processes (such as deployments and upgrades) Debug production issues across all services and levels of the stack Identify improvements for the product architecture from the reliability, performance and availability perspectives Plan the growth of Together AI’s infrastructure Requirements 7+ years of professional SRE or related experience Ideally 2 years as a Lead SRE Bachelor's degree in Computer Science or a related field or equivalent work experience Expert knowledge of Ansible (roles, playbooks), Terraform, and Kubernetes Proficiency in programming/scripting languages Direct experience in monitoring and observability practices Advanced knowledge of cloud services Ability to thrive in a collaborative environment involving different stakeholders and subject matter experts About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tu

Role Overview As a Systems Research Engineer Intern specialized in GPU Programming, you will play a crucial role in developing and optimizing GPU-accelerated kernels and algorithms for ML/AI applications. Working closely with the modeling and algorithm team, you will co-design GPU kernels and model architecture to enhance the performance and efficiency of our AI systems. Collaborating with the hardware and software teams, you will contribute to the co-design of efficient GPU architectures and programming models, leveraging your expertise in GPU programming and parallel computing. Your research skills will be vital in staying up-to-date with the latest advancements in GPU programming techniques, ensuring that our AI infrastructure remains at the forefront of innovation. This internship is based on-site at our San Francisco HQ, running through the Winter term from January to April. Responsibilities Optimize and fine-tune GPU code to achieve better performance and scalability Collaborate with cross-functional teams to integrate GPU-accelerated solutions into existing software systems Stay up-to-date with the latest advancements in GPU programming techniques and technologies Requirements Strong background in GPU programming and parallel computing, such as CUDA and/or Triton. Knowledge of ML/AI applications and models Knowledge of performance profiling and optimization tools for GPU programming Excellent problem-solving and analytical skills About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Internship Program Details:&n

Role Overview As a Systems Research Engineer Intern specialized in GPU Programming, you will play a crucial role in developing and optimizing GPU-accelerated kernels and algorithms for ML/AI applications. Working closely with the modeling and algorithm team, you will co-design GPU kernels and model architecture to enhance the performance and efficiency of our AI systems. Collaborating with the hardware and software teams, you will contribute to the co-design of efficient GPU architectures and programming models, leveraging your expertise in GPU programming and parallel computing. Your research skills will be vital in staying up-to-date with the latest advancements in GPU programming techniques, ensuring that our AI infrastructure remains at the forefront of innovation. Responsibilities Optimize and fine-tune GPU code to achieve better performance and scalability Collaborate with cross-functional teams to integrate GPU-accelerated solutions into existing software systems Stay up-to-date with the latest advancements in GPU programming techniques and technologies Requirements Strong background in GPU programming and parallel computing, such as CUDA and/or Triton. Knowledge of ML/AI applications and models Knowledge of performance profiling and optimization tools for GPU programming Excellent problem-solving and analytical skills About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Internship Program Details: Our internship program runs 12 to 14 weeks, giving you the opportunity to work alongside industry-leading en

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

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

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

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

About the Role As a Systems Research Engineer specialized in GPU Programming, you will play a crucial role in developing and optimizing GPU-accelerated kernels and algorithms for ML/AI applications. Working closely with the modeling and algorithm team, you will co-design GPU kernels and model architecture to enhance the performance and efficiency of our AI systems. Collaborating with the hardware and software teams, you will contribute to the co-design of efficient GPU architectures and programming models, leveraging your expertise in GPU programming and parallel computing. Your research skills will be vital in staying up-to-date with the latest advancements in GPU programming techniques, ensuring that our AI infrastructure remains at the forefront of innovation. Requirements Strong background in GPU programming and parallel computing, such as CUDA and/or Triton. Knowledge of ML/AI applications and models Knowledge of performance profiling and optimization tools for GPU programming Excellent problem-solving and analytical skills Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or equivalent practical experiences Responsibilities Optimize and fine-tune GPU code to achieve better performance and scalability Collaborate with cross-functional teams to integrate GPU-accelerated solutions into existing software systems Stay up-to-date with the latest advancements in GPU programming techniques and technologies About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Compensation We offer competi

About the job Own the reliability of AI compute hardware where lab discovery becomes data centre reality. Graphcore is building the hardware and systems infrastructure needed for the next generation of AI breakthroughs. This role helps prove those platforms work when complexity, scale and pressure increase. You will lead advanced operational, diagnostic and engineering support across lab and data centre environments. Your work will improve hardware bring-up, validation and troubleshooting for AI compute platforms. You will diagnose failures across server blades, racks, power systems, thermal behaviour, network configuration and BIOS/BMC issues. You will turn hard problems into clear root cause analysis, corrective action and better operating practice. This is hands-on engineering with visible impact. You will support new platforms from early bring-up through deployment, helping Graphcore move faster with confidence. The team and culture You will work with Systems Engineering, Hardware Engineering, server engineering, firmware, platform architects and data centre operations. Problems are solved close to the hardware, with clear evidence and direct ownership. The team moves quickly during bring-up and validation cycles. Engineers are expected to act fast, take responsibility and speak up when something needs attention. Decisions are shaped by data, system behaviour and practical engineering judgement. You will guide others, improve documentation and help raise the standard of hardware diagnostics. What we're looking for · Strong knowledge of server hardware architectures and board-level debugging · Experience isolating failures using system logs, telemetry, power data and thermal metrics · Hands-on experience with HPC systems, AI compute platforms or rack-scale infrastructure · Ability to lead structured root cause analysis and propose practical corrective actions · Confidence working across engineering, platform and data centre teams · Clear written and verbal

At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀 Job Summary As a Business Systems Engineer on the GTM Engineering team, you will design, build, and operate the automation and AI systems that power ClickUp's Go-To-Market business — spanning core business products and the integration platforms that connect them. This role is AI-native at its core: you won't just maintain existing workflows, you'll actively advance our GTM systems with intelligent agents, LLM-powered automations, and next-generation integration patterns built on MCP, ClickUp Super Agents, and modern iPaaS tooling. You'll sit at the intersection of business systems architecture and applied AI — partnering with Sales, Finance, Revenue Operations, and fellow GTM Systems engineers to eliminate toil, accelerate revenue workflows, and build the automated, AI-augmented infrastructure the company runs on. This is a hands-on engineering role for someone deeply fluent in enterprise business systems, excited about deploying production AI, and committed to genuine ownership of the platforms they build — directly supporting GTMSOE's broader mission of operational excellence across the GTM org. Key Responsibilities AI-Native Automation & Agent Development Design and build AI-powered automations and agentic workflows across the GTM tech stack — including Salesforce, NetSuite, Workato, and MuleSoft — to eliminate manual effort and accelerate business operations. Develop and deploy ClickUp Super Agents and LLM-based automations to automate tasks such as deal data enrichment, quote generation assistance, order validation, revenue recognition triggers, and exception handling in quote-to-cash workflow

At Vanta, our mission is to help businesses earn and prove trust. We believe that security should be monitored and verified continuously, and we empower companies to practice better security and prove it with ease. Vanta has a kind and talented team, and while some have prior security experience, many have been successful at Vanta without it. Vanta's Corporate Engineering team is the infrastructure layer that keeps 1,500+ people connected, secure, and moving fast. This role leads the shift from a reactive support function to a platform function the whole company relies on. Corporate Engineering owns the systems, tooling, and access infrastructure Vanta runs on. From identity and device management to AI tooling infrastructure and ITSM, CE makes sure Vanta's people have the right access, the right tools, and the right guardrails on day one and every day after. CE is at an inflection point. The company has scaled quickly, AI tooling is live company-wide, and the team is ready for a leader who sets technical direction, establishes clear ownership, and builds the leadership layer beneath them. As Sr. Manager, Systems Engineering, you will lead a team of experienced engineers, own several of the company's highest-priority platform initiatives, and make CE a function the rest of Vanta builds on rather than routes around. What you’ll do as a Senior Manager, Systems Engineering at Vanta: Lead, coach, and grow a team of systems engineers and a tech lead. Set clear expectations, hold a high bar, and develop the people who build Vanta's most critical internal systems. Own joiner, mover, and leaver automation end to end in Okta Workflows, so access is correct on day one, follows people through role changes, and closes completely on departure, including API keys and service credentials. Build and own device assurance across macOS and Windows, defining OS, browser, and device-state policy ahead of enforcement rather than in response to it. Rationalize the ITSM estate so self-servi

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 We’re looking for an experienced systems software engineer to help define and build the host software stack for our custom next-generation AI systems. You will work close to the hardware on performance-critical software, including Linux kernel drivers, high-throughput I/O paths, and system-scale networking and RDMA. This role spans architecture, implementation, platform bring-up, debugging, and performance optimization. You will work across hardware and software boundaries to make new systems usable end to end, from low-level device interfaces through userspace tooling and production validation. In this role you will: Design, implement, and debug host-side systems software for AI infrastructure, including Linux kernel drivers and supporting userspace components. Build and optimize software paths for high-throughput, low-latency communication, including RDMA and related networking functionality. Develop software around PCIe, DMA, NICs, accelerators, memory movement, and device interaction. Bring up new hardware platforms and diagnose complex issues across kernel, firmware, networking, and hardware boundaries. Build tooling for integration, testing, diagnostics, observability, qualification, and performance characterization. Collaborate with hardware, networking, and platform teams to define interfaces and integrate new capabilities. Work with external vendors where needed to integrate technologies and drive issues to resolution. Contribute across the systems sof