About Nebius:
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.
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.
Our customers build the frontier of AI on top of Nebius — 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 — 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 — and at our scale, every slice is mission-critical.
If you want to help build the best AI cloud in the world — 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 — this team is for you.
The platform you'll help build:
- Hardware platforms & launch — bringing new GPU and CPU platforms (GB300, Vera Rubin, ARM/Grace, future generations) to production with full launch readiness across the stack.
- Cluster lifecycle & fleet operations — new region launches, 100,000+ GPU cluster bring-up, platform sharding and allocation architecture, release engineering, host-lifecycle automation, operational efficiency.
- Reliability & Mission Control — autohealing, health checks, SLA, fault-tolerant training, MTTR reduction, customer trust at scale, observability as a product.
- Customer experience & developer surface — Compute APIs, console, CLI, IMDS and in-VM signals, self-service workflows, notifications, customer-facing observability, unified UX across the product line.
- GPU & InfiniBand foundational services — drivers, firmware, NCCL, IB/RoCE, NVLink topology, the foundational layer everything else builds on.
- Managed runtime platforms — Soperator (Slurm-on-Kubernetes) and MK8S (Managed Kubernetes for AI workloads), powering training and inference for frontier labs.
- Platform integrations & emerging workloads — Token Factory integration, RL and agentic workload infrastructure, capacity sharing, new business surfaces as they emerge.
- Cross-platform program & delivery — NVIDIA partnership programs, major-maintenance orchestration, cross-stream releases.
Your responsibilities will include (regardless of which slice you own):
- 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.
Customer-facing experience and lived-it perspective:
- 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).
About Nebius
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.
Nebius AI owns a data center in Finland, built from the ground up by the company’s 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).
Nebius’s headquarters are in Amsterdam, Netherlands, with teams working out of R&D hubs across Europe and the Middle East.
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 – from hardware to UI – 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’re growing and expanding our products every day.
If you’re up to the challenge and are excited about AI and ML as much as we are, join us!
Benefits & Perks:
- 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
What's it like to work at Nebius:
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI
Equal Opportunity Statement:
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.
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.
If you need accommodations during the application process, please let us know.
Skills Required
- 6+ years in Product Management, Platform PM, Infrastructure PM, or SRE/Engineering Lead with strong product instincts.
- Strong technical foundation and cloud-infrastructure depth (API semantics, control-plane vs data-plane, multi-tenant operations).
- Experience with cloud, GPU, or HPC infrastructure at meaningful scale (thousands of nodes, multi-region, multi-tenant).
- Track record of shipping technically complex platform products with measurable customer or platform impact.
- Strong analytical skills: defining and instrumenting product metrics and building data-informed roadmaps.
- Experience leading discovery-heavy work: structured customer interviews, usage analytics, support-ticket analysis.
- Strong communication and ability to align engineering, SRE, customer-facing teams, and executive stakeholders.
- High ownership, bias to ship, comfort with operational reality, and willingness to push back on engineering when needed.
- 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 distributed ML training and inference workflows (multi-node, multi-GPU, checkpointing, NCCL, fault-tolerant training).
- Experience as a customer/operator of AI cloud infrastructure (internal ML platform experience).
- Direct experience with NVIDIA reference architectures and stack (DGX, MGX, NVL72, SuperPOD, drivers, CUDA, NCCL, DCGM).
- Familiarity with InfiniBand / RoCE fabrics, firmware lifecycle, and topology-aware scheduling.
- Hands-on experience managing 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 end-to-end data-center bring-ups.
- Background in release engineering, change management, or major-maintenance orchestration in production.
- Experience designing console / CLI / API surfaces at hyperscaler quality (versioning, idempotency, error semantics).
- Experience with observability products (Grafana, Datadog, Honeycomb, New Relic).
- Knowledge of customer trust artifacts: 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 and reliability metrics (Goodput, MFU, MTTR, MTBF).
- Experience with autohealing systems and graceful failure semantics.
- Familiarity with RL/agentic/inference workload patterns and tooling (vLLM, SGLang, Ray, Token Factory-style serving).
- Experience with multi-product cloud integrations, capacity sharing, billing models, and cross-product packaging.
- Background in pricing strategy for infrastructure products (reserved/on-demand/preemptible tiers, two-part pricing).
What We Do
Cloud platform specifically designed to train AI models
