{"id":325524,"date":"2026-06-22T00:00:00","date_gmt":"2026-06-21T23:00:00","guid":{"rendered":"https:\/\/www.remotetribe.life\/remotejobs\/job\/technical-product-manager-ai-compute-platform\/"},"modified":"2026-06-22T00:00:00","modified_gmt":"2026-06-21T23:00:00","slug":"technical-product-manager-ai-compute-platform","status":"publish","type":"job_listing","link":"https:\/\/www.remotetribe.life\/remotejobs\/job\/technical-product-manager-ai-compute-platform\/","title":{"rendered":"Technical Product Manager \u2013 AI Compute Platform"},"content":{"rendered":"
\n

About Nebius:<\/strong><\/p>\n

Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI\/ML infrastructure.<\/p>\n

Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.<\/p>\n

Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.<\/p>\n<\/div>\n

The role<\/strong><\/div>\n
Our customers build the frontier of AI on top of Nebius \u2014 training state-of-the-art models, running production inference at scale, shipping the research and products that define where the field is going next.
We are building the AI cloud that the people building the frontier of AI choose deliberately \u2014 not on price, not on raw capacity, but on how it works to use it day to day. To do that, we are growing the AI Compute Platform product team and hiring multiple Technical Product Managers across the full surface of the platform.
Your scope will be defined by what you bring. We will match your technical strengths, customer experience, and product instincts to the area of the platform where you can have the most impact. The platform is broad \u2014 and at our scale, every slice is mission-critical.
If you want to help build the best AI cloud in the world \u2014 and you have the technical depth to engage engineering leaders as a peer (not as a translator) and the comfort to talk to customers directly \u2014 this team is for you.<\/div>\n
The platform you’ll help build:<\/strong><\/div>\n
    \n
  • Hardware platforms & launch \u2014 bringing new GPU and CPU platforms (GB300, Vera Rubin, ARM\/Grace, future generations) to production with full launch readiness across the stack.<\/li>\n
  • Cluster lifecycle & fleet operations \u2014 new region launches, 100,000+ GPU cluster bring-up, platform sharding and allocation architecture, release engineering, host-lifecycle automation, operational efficiency.<\/li>\n
  • Reliability & Mission Control \u2014 autohealing, health checks, SLA, fault-tolerant training, MTTR reduction, customer trust at scale, observability as a product.<\/li>\n
  • Customer experience & developer surface \u2014 Compute APIs, console, CLI, IMDS and in-VM signals, self-service workflows, notifications, customer-facing observability, unified UX across the product line.<\/li>\n
  • GPU & InfiniBand foundational services \u2014 drivers, firmware, NCCL, IB\/RoCE, NVLink topology, the foundational layer everything else builds on.<\/li>\n
  • Managed runtime platforms \u2014 Soperator (Slurm-on-Kubernetes) and MK8S (Managed Kubernetes for AI workloads), powering training and inference for frontier labs.<\/li>\n
  • Platform integrations & emerging workloads \u2014 Token Factory integration, RL and agentic workload infrastructure, capacity sharing, new business surfaces as they emerge.<\/li>\n
  • Cross-platform program & delivery \u2014 NVIDIA partnership programs, major-maintenance orchestration, cross-stream releases.<\/li>\n<\/ul>\n
    You will own one of the slices of this platform end-to-end \u2014 from strategy and roadmap through delivery, adoption, and measurable outcomes.
    Your responsibilities will include (regardless of which slice you own):<\/div>\n
      \n
    • Own end-to-end product responsibility for your area \u2014 strategy, roadmap, discovery, delivery, adoption, measurable customer and platform outcomes.<\/li>\n
    • Design and own the platform contracts customers depend on \u2014 APIs, semantics, system events, customer-facing surfaces, operational behavior \u2014 at hyperscaler quality.<\/li>\n
    • Drive cross-team execution across platform engineering, networking, storage, Soperator\/MK8S, observability, IAM, billing, capacity planning, support, and product design.<\/li>\n
    • Turn customer pain into product commitments through structured discovery \u2014 interviews, usage analytics, support patterns, incident postmortems. Close the loop so the same class of failure or friction does not recur.<\/li>\n
    • Engage engineering as a technical peer \u2014 debate API design, reason about system trade-offs, judge the quality of platform internals, and push back when the design is wrong.<\/li>\n
    • Define and own success metrics \u2014 what you ship is measured by what changed for the customer or the platform, not by the size of the spec.<\/li>\n
    • Be the product voice that customer-facing teams (Support, CX, TAMs) escalate to when a system behavior, API contract, or operational pattern needs a product decision, not a workaround.<\/li>\n<\/ul>\n
      We expect you to have:<\/strong><\/div>\n
        \n
      • 6+ years in Product Management, Platform PM, Infrastructure PM, or SRE \/ Engineering Lead with strong product instincts.<\/li>\n
      • Strong technical foundation and cloud-infrastructure depth \u2014 comfort reasoning about API semantics, control-plane vs data-plane behavior, system events and lifecycle, multi-tenant operational realities. You can engage engineering leaders as a peer, not as a translator.<\/li>\n
      • Experience with cloud, GPU, or HPC infrastructure \u2014 either building one or operating one at meaningful scale (thousands of nodes, multi-region, multi-tenant).<\/li>\n
      • Track record of shipping technically complex platform products with measurable customer or platform impact \u2014 quantitative results, not aspirational bullets.<\/li>\n
      • Strong analytical skills: comfort defining and instrumenting product metrics, working with telemetry, building data-informed roadmaps.<\/li>\n
      • Experience leading discovery-heavy work \u2014 structured customer interviews, usage analytics, support-ticket analysis \u2014 and turning insights into shipped product.<\/li>\n
      • Strong communication and ability to align engineering, SRE, customer-facing teams, and exec stakeholders.<\/li>\n
      • High ownership, bias to ship, comfort with messy operational reality, and the instinct to push back on engineering when the customer experience or platform quality would suffer.<\/li>\n<\/ul>\n
        It will be an added bonus if you have (these are not all required \u2014 different strengths fit different slices of the platform):
        Customer-facing experience and lived-it perspective:<\/em><\/div>\n
          \n
        • Direct PM experience with frontier AI customers \u2014 ML platform teams, MLOps engineers, training and inference at scale.<\/li>\n
        • Familiarity with Kubernetes, Slurm, or HPC environments from the user side, and ML training workflows.<\/li>\n
        • Hands-on experience with ML training and inference workflows \u2014 especially distributed training at scale (multi-node, multi-GPU; comfort with checkpointing, NCCL, fault-tolerant training, debugging large training jobs).<\/li>\n
        • Experience as a customer of AI cloud infrastructure at large scale \u2014 especially as part of an internal ML platform team that built and operated infrastructure for ML engineers inside your own company. If you have lived through what frustrates customers about clouds, you will know exactly what we are trying to fix.<\/li>\n<\/ul>\n
          Hardware, GPU, and HPC depth:<\/em><\/div>\n
            \n
          • Direct experience with NVIDIA reference architectures (NVL72, SuperPOD, MGX, DGX) and the NVIDIA stack (drivers, CUDA, NCCL, DCGM).<\/li>\n
          • Familiarity with InfiniBand \/ RoCE fabrics, firmware lifecycle, topology-aware scheduling.<\/li>\n
          • Hands-on with GPU clusters or HPC fabrics at thousands-of-nodes scale.<\/li>\n<\/ul>\n
            Cluster and fleet operations:<\/em><\/div>\n
              \n
            • Background in Kubernetes lifecycle (CAPI, cluster upgrades, node-pool management) or Slurm at scale.<\/li>\n
            • Experience launching new cloud regions or data-center bring-ups end-to-end.<\/li>\n
            • Background in release engineering, change management, or major-maintenance orchestration in production environments.<\/li>\n<\/ul>\n
              Customer experience and developer surface:<\/em><\/div>\n
                \n
              • Exposure to console \/ CLI \/ API design at hyperscaler quality \u2014 AWS, GCP, Azure depth on consistency, versioning, idempotency, error semantics, deprecation policy.<\/li>\n
              • Background in observability product \u2014 Grafana, Datadog, Honeycomb, New Relic.<\/li>\n
              • Knowledge of customer trust artefacts \u2014 status pages, RCA workflows, audit logs, SLA reporting, maintenance notifications.<\/li>\n
              • Familiarity with developer-experience product patterns \u2014 Stripe, Cloudflare, Vercel, Supabase, Render.<\/li>\n<\/ul>\n
                Reliability and operational outcomes:<\/em><\/div>\n
                  \n
                • Background in SRE, reliability engineering, or fault-tolerant systems for paying customers.<\/li>\n
                • Familiarity with reliability metrics that matter: Goodput, MFU, MTTR, MTBF.<\/li>\n
                • Experience with autohealing systems and graceful failure semantics.<\/li>\n<\/ul>\n
                  Emerging workloads and integrations:<\/em><\/div>\n
                    \n
                  • Familiarity with RL \/ agentic \/ inference workload patterns \u2014 vLLM, SGLang, Ray, Token Factory-style serving, sandbox technologies (Firecracker, gVisor, Kata).<\/li>\n
                  • Experience with multi-product cloud integrations \u2014 capacity sharing, billing models, cross-product packaging.<\/li>\n
                  • Background in pricing strategy for infrastructure products (reserved \/ on-demand \/ preemptible tiers, two-part pricing).<\/li>\n<\/ul>\n

                    About Nebius<\/strong><\/p>\n

                    Nebius AI is an AI cloud platform with one of the largest GPU capacities in Europe. Launched in November 2023, the Nebius AI platform provides high-end, training-optimized infrastructure for AI practitioners. As an NVIDIA preferred cloud service provider, Nebius AI offers a variety of NVIDIA GPUs for training and inference, as well as a set of tools for efficient multi-node training.\u00a0<\/p>\n

                    Nebius AI owns a data center in Finland, built from the ground up by the company\u2019s R&D team and showcasing our commitment to sustainability. The data center is home to ISEG, the most powerful commercially available supercomputer in Europe and the 16th most powerful globally (Top 500 list, November 2023). <\/p>\n

                    Nebius\u2019s headquarters are in Amsterdam, Netherlands, with teams working out of R&D hubs across Europe and the Middle East.\u00a0<\/p>\n

                    Nebius AI is built with the talent of more than 500 highly skilled engineers with a proven track record in developing sophisticated cloud and ML solutions and designing cutting-edge hardware. This allows all the layers of the Nebius AI cloud \u2013 from hardware to UI \u2013 to be built in-house, distictly differentiating Nebius AI from the majority of specialized clouds: Nebius customers get a true hyperscaler-cloud experience tailored for AI practitioners. We\u2019re growing and expanding our products every day.\u00a0<\/p>\n

                    If you\u2019re up to the challenge and are excited about AI and ML as much as we are, join us!<\/p>\n

                    \n

                    Benefits & Perks:<\/strong><\/p>\n

                      \n
                    • Competitive compensation<\/li>\n
                    • Career growth and learning opportunities<\/li>\n
                    • Flexibility and ownership<\/li>\n
                    • Collaborative and innovative culture<\/li>\n
                    • Opportunity to work on impactful AI projects<\/li>\n
                    • International environment and talented teams<\/li>\n<\/ul>\n

                      What’s it like to work at Nebius:<\/strong><\/p>\n

                      Fast moving\u00a0– Bold thinking\u00a0– Constant growth\u00a0– Meaningful impact\u00a0– Trust and real ownership\u00a0– Opportunity to shape the future of AI\u00a0<\/p>\n

                      Equal Opportunity Statement:<\/strong><\/p>\n

                      Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.<\/p>\n

                      Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.\u00a0<\/p>\n

                      If you need accommodations during the application process, please let us know.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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