Jobs at Nebius (Now Hiring) — 71 open — Page 3

• 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.

• 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.

• 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!