Staff Software Engineer, Inference / Compute Infrastructure Engineering London or Amsterdam

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

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Build the future of data. Join the Snowflake team. The Snowflake Machine Learning Platform team’s mission is to enable customers to bring their machine learning and deep learning workloads to Snowflake. Our customers want to build powerful models with the ever-increasing data in Snowflake but face several challenges including infrastructure optimizations, orchestration, performance, and security. The team aims to solve these challenges by building highly integrated platform solutions that are simple, secure, and enable end-to-end ML workflows. We are on an early journey to build the most scalable machine learning and data platform without sacrificing the benefits of a single platform and governance. We are looking for outstanding technical leaders who will join our ML Platform team to build the next-generation platform and play a pivotal role in this journey by understanding Snowflake’s core platform architecture and evolving it to enable state-of-the-art machine learning and LLM workloads. Join us to define strategies, set technical directions, design and execute, engage and deliver innovation, and unlock the power of AI for thousands of enterprise customers. This position is based in Menlo Park, CA, and Bellevue, WA. RESPONSIBILITIES : Help define and own the roadmap, wor

About the job Own the test strategy that proves Graphcore’s next generation drivers are production-ready. As a Staff System Software Engineer, you will shape how Graphcore validates complex device drivers and platform tools. You will bring technical depth across Linux kernels, drivers and systems software. Your work will help raise the quality bar for products that support the next generation of AI compute. You will influence design decisions, guide test infrastructure and drive reliability across the stack. You will design, build, execute and report tests across component, system and acceptance levels. You will debug difficult issues using hardware and software tools, simulators and emulators. This is a hands-on technical leadership role with broad reach. You will help teams move quickly while protecting production quality. The team and culture The Drivers QA team verifies low-level kernel drivers, user-space libraries and platform tools. Work spans component tests, user acceptance and full system validation. You will work directly with driver developers, hardware, firmware, ML stack, Kubernetes and cloud management teams. Decisions are made close to the technical detail, with engineers expected to speak up and own outcomes. The team values engineers who think big, act fast and take responsibility. You will mentor others, raise standards and turn hard system problems into lasting improvements. What we're looking for · Deep experience validating Linux kernels, device drivers or similar systems software · Strong C++ and Python test development skills · Experience with CI/CD, build tools and modern automated testing practices · Ability to debug complex issues across hardware, firmware, drivers and upper software layers · Proven ownership of technical work, priorities and delivery in an agile engineering setting · Clear communication skills, including the ability to document and explain complex technical concepts While we have outlined a set of requirements, we value t

About the job Own the test strategy that proves Graphcore’s next generation drivers are production-ready. As a Staff System Software Engineer, you will shape how Graphcore validates complex device drivers and platform tools. You will bring technical depth across Linux kernels, drivers and systems software. Your work will help raise the quality bar for products that support the next generation of AI compute. You will influence design decisions, guide test infrastructure and drive reliability across the stack. You will design, build, execute and report tests across component, system and acceptance levels. You will debug difficult issues using hardware and software tools, simulators and emulators. This is a hands-on technical leadership role with broad reach. You will help teams move quickly while protecting production quality. The team and culture The Drivers QA team verifies low-level kernel drivers, user-space libraries and platform tools. Work spans component tests, user acceptance and full system validation. You will work directly with driver developers, hardware, firmware, ML stack, Kubernetes and cloud management teams. Decisions are made close to the technical detail, with engineers expected to speak up and own outcomes. The team values engineers who think big, act fast and take responsibility. You will mentor others, raise standards and turn hard system problems into lasting improvements. What we're looking for · Deep experience validating Linux kernels, device drivers or similar systems software · Strong C++ and Python test development skills · Experience with CI/CD, build tools and modern automated testing practices · Ability to debug complex issues across hardware, firmware, drivers and upper software layers · Proven ownership of technical work, priorities and delivery in an agile engineering setting · Clear communication skills, including the ability to document and explain complex technical concepts While we have outlined a set of requirements, we value t

As a Staff Engineer on Datadog's Compute – Disruption and Workload Placement team, you'll help define how our Kubernetes fleet scales to meet the demands of rapidly growing AI and cloud-native workloads. You'll work on the systems that ensure engineering teams have the right compute capacity, in the right region, at the right time across AWS, Google Cloud, and Azure. This is a highly technical, high-impact role where you'll shape the future of capacity orchestration, influence platform architecture, and solve infrastructure challenges that directly support Datadog's continued growth. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You'll Do: Lead the technical direction of capacity management and workload placement for Datadog's Kubernetes platform spanning 100,000+ virtual machines across multiple cloud providers. Design and build systems that optimize how engineering workloads are scheduled and deployed across regions while balancing capacity constraints, reliability, and performance. Partner across infrastructure teams to evolve multi-region and multi-cloud capacity orchestration as Datadog continues to scale. Develop production software in Go to improve Kubernetes platform capabilities, automation, and operational efficiency. Use data and capacity signals to influence infrastructure decisions, forecast growth, and improve workload placement strategies. Who You Are: You have significant experience designing and operating large-scale Kubernetes-based infrastructure or platform systems. You are an experienced software engineer with strong programming skills, ideally in Go or a comparable systems programming language. You have hands-on experience with at least one major cloud provider (AWS, Google Cloud, or Azure) and understand distributed cloud infrastructure. Yo

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. We are looking for a highly technical Senior Staff/Principal Engineer to lead the porting and enablement of critical AI workloads. You will be a primary driver in migrating compute workloads to RISC-V architectures, ensuring our hardware is optimized for real-world application performance. The ideal candidate has a strong background in DevOps, workload porting, or application enablement . While this is an individual contributor role at its core, you will have the opportunity to grow and lead a small, specialized team over time as our workload migration efforts scale. This role is remote, based in the United States or Australia. 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 A Technical Catalyst: You thrive on the challenge of driving AI hardware porting to RISC-V and making complex software stacks run efficiently on new hardware. Systems Expert: You possess deep knowledge of system software, compilers, or low-level OS internals. You are an expert in ARM or x86 environments and are ready to apply those skills to the RISC-V frontier. A Project Driver: You have the technical authority to lead the implementation of a compute migration to RISC-V through