Technical product manager in AI compute platform

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. 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.

• Own end-to-end product responsibility for your area — strategy, roadmap, discovery, delivery, adoption, measurable customer and platform outcomes.
• Design and own the platform contracts customers depend on — APIs, semantics, system events, customer-facing surfaces, operational behavior — at hyperscaler quality.
• Drive cross-team execution across platform engineering, networking, storage, Soperator/MK8S, observability, IAM, billing, capacity planning, support, and product design.
• Turn customer pain into product commitments through structured discovery — interviews, usage analytics, support patterns, incident postmortems. Close the loop so the same class of failure or friction does not recur.
• Engage engineering as a technical peer — debate API design, reason about system trade-offs, judge the quality of platform internals, and push back when the design is wrong.
• Define and own success metrics — what you ship is measured by what changed for the customer or the platform, not by the size of the spec.
• 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.

• 6+ years in Product Management, Platform PM, Infrastructure PM, or SRE / Engineering Lead with strong product instincts.
• Strong technical foundation and cloud-infrastructure depth — 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.
• Experience with cloud, GPU, or HPC infrastructure — either building one or operating one at meaningful scale (thousands of nodes, multi-region, multi-tenant).
• Track record of shipping technically complex platform products with measurable customer or platform impact — quantitative results, not aspirational bullets.
• Strong analytical skills: comfort defining and instrumenting product metrics, working with telemetry, building data-informed roadmaps.
• Experience leading discovery-heavy work — structured customer interviews, usage analytics, support-ticket analysis — and turning insights into shipped product.
• Strong communication and ability to align engineering, SRE, customer-facing teams, and exec stakeholders.
• 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.
• Будет плюсом: Direct PM experience with frontier AI customers — ML platform teams, MLOps engineers, training and inference at scale; Familiarity with Kubernetes, Slurm, or HPC environments from the user side, and ML training workflows; Hands-on experience with ML training and inference workflows — especially distributed training at scale (multi-node, multi-GPU; comfort with checkpointing, NCCL, fault-tolerant training, debugging large training jobs); Experience as a customer of AI cloud infrastructure at large scale — 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; Direct experience with NVIDIA reference architectures (NVL72, SuperPOD, MGX, DGX) and the NVIDIA stack (drivers, CUDA, NCCL, DCGM); Familiarity with InfiniBand / RoCE fabrics, firmware lifecycle, topology-aware scheduling; Hands-on with GPU clusters or HPC fabrics at thousands-of-nodes scale; Background in Kubernetes lifecycle (CAPI, cluster upgrades, node-pool management) or Slurm at scale; Experience launching new cloud regions or data-center bring-ups end-to-end; Background in release engineering, change management, or major-maintenance orchestration in production environments; Exposure to console / CLI / API design at hyperscaler quality — AWS, GCP, Azure depth on consistency, versioning, idempotency, error semantics, deprecation policy; Background in observability product — Grafana, Datadog, Honeycomb, New Relic; Knowledge of customer trust artefacts — status pages, RCA workflows, audit logs, SLA reporting, maintenance notifications; Familiarity with developer-experience product patterns — Stripe, Cloudflare, Vercel, Supabase, Render; Background in SRE, reliability engineering, or fault-tolerant systems for paying customers; Familiarity with reliability metrics that matter: Goodput, MFU, MTTR, MTBF; Experience with autohealing systems and graceful failure semantics; Familiarity with RL / agentic / inference workload patterns — vLLM, SGLang, Ray, Token Factory-style serving, sandbox technologies (Firecracker, gVisor, Kata); Experience with multi-product cloud integrations — capacity sharing, billing models, cross-product packaging; Background in pricing strategy for infrastructure products (reserved / on-demand / preemptible tiers, two-part pricing)

• Competitive compensation
• Career growth and learning opportunities
• Flexibility and ownership
• Collaborative and innovative culture
• Opportunity to work on impactful AI projects
• International environment and talented teams
• Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI