Senior Technical Product Manager - Compute API & Experience

• Strategy & roadmap for the Compute API & Experience area — discovery, delivery, adoption.
• The Compute API as a product — contract, versioning, idempotency, error semantics, deprecation; predictable reconciliation / desired-state behaviour.
• VM / instance lifecycle as a first-class API — start / stop / restart / recovery, maintenance behaviour — the behaviour customers actually experience.
• Scheduling & placement - capacity-aware placement, NER handling, operation priority and fairness — and how that logic surfaces to customers.
• Integration and interaction with adjacent platform services — storage, networking, IAM and others
• The customer-facing surface end-to-end — console, CLI / SDK, instance metadata, self-service, notifications.
• New compute platforms into the product — instance types, presets and platform capabilities — with the hardware and platform teams.
• A multi-region product experience — region-aware placement and a project model that works across regions the way customers expect.

• Own end-to-end product responsibility for your area — strategy, roadmap, discovery, delivery, adoption, and measurable customer & platform outcomes.
• Design and govern platform contracts at hyperscaler quality.
• Co-design the compute control plane with engineering as a technical peer (scheduling, allocation, reconciliation) — not just word an API.
• Manage stakeholders and drive cross-team execution across engineering, networking, storage, product and sales / CX.
• Define success metrics for the Compute API and the customer experience, and be the escalation point for product decisions on your surface.

1. 6+ years in Product / Platform / Infrastructure PM — or an SRE / Engineering Lead moving to product — shipping technically complex platform products with measurable impact.
2. Owned a public cloud / compute / platform API as a product. A declarative / desired-state or control-plane API (Kubernetes CRDs / operators, or a cloud control plane) is a strong plus.
3. Hands-on cloud experience — the VM / instance lifecycle, its API and its console at a public or large-scale cloud (AWS / GCP / Azure or another large-scale cloud), with a real under-the-hood understanding of how scheduling, allocation and the virtualization layer work (control-plane vs data-plane — how VMs are placed, scheduled and run). GPU / AI-cloud experience is a big plus, but not required.
4. A systems understanding of how a cloud fits together — how compute, storage, networking, IAM and related services connect and interact, and how it all works under the hood — enough to design the Compute API coherently and reason about it with those teams.
5. Gatekeeper craft — other teams have shipped features through an API you governed: design review, breaking-change control, pushback with a migration path; debates trade-offs with engineering as a peer.
6. Strong analytical skills — comfort defining and instrumenting product metrics, working with telemetry, and building data-informed roadmaps.
7. Experience leading discovery-heavy work — structured customer interviews (we build top-tier infrastructure and work directly with frontier AI labs), usage analytics — turning insights into shipped product.
8. Strong communication and the ability to align engineering, SRE, customer-facing teams and exec stakeholders.
9. High ownership, a bias to ship, and focus on outcomes and customer value.

• System design of compute / platform services — control plane, scheduling, allocation, reconciliation.
• Depth in VM lifecycle semantics (stop / start / restart / recovery, failure & stop reasons).
• Observability / SLI-SLO delivered as a product; status pages / RCA / SLA artefacts.
• Console / CLI / API design and developer-experience product craft.

• Hands-on with GPUs / accelerated compute and ML / AI workloads (hands-on ML experience).
• Multi-node GPU clusters and their interconnect — InfiniBand / RoCE fabrics, NVLink topology, topology-aware placement.
• NVIDIA reference architectures; brought a new instance type / preset / hardware platform into a cloud's compute catalog.

• Understanding of virtualization internals (hypervisors / KVM / QEMU, VM internals).
• Hands-on experience with Kubernetes.

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!