Technical Product Manager

The role\u00a0\u2013 We are looking for a Technical Product Manager to own product direction for Soperator — our Slurm-on-Kubernetes control plane for GPU clusters. In this role you will shape how ML engineers and research teams run, scale, and optimize distributed workloads in production. Your responsibilities will include: Own the full user journey across Soperator clusters: Slurm workflows, dashboards, alerts/notifications, node lifecycle, and training/inference capacity management. Define product direction end-to-end: problem discovery 7 solution design 7 delivery 7 adoption. Lead deep customer discovery through interviews, usage analytics, and workload analysis to uncover high-impact opportunities. Drive execution across platform teams: compute, networking, storage, observability, IAM and etc. Translate frontier ML and infrastructure ideas into practical product capabilities for real-world GPU clusters. Define success metrics, prioritize roadmap decisions with data, and ensure measurable customer/business impact. Lead the open-source strategy and execution for Soperator: shape public roadmap themes, prioritize OSS-facing capabilities, and ensure strong adoption in the community. We expect you to have: 3–5+ years in Product Management, ML infrastructure/MLOps, distributed systems, or cloud platform engineering. Strong technical depth in distributed systems, cloud infrastructure, or ML platforms. Hands-on familiarity with large-scale ML training and orchestration tools (e.g., Slurm, Kubernetes, Ray). Track record of shipping technically complex products with multiple engineering teams. Strong communication and stakeholder management across engineering, research, and customers. Experience with product analytics, data-informed prioritization, and experimentation. High ownership, high learning velocity, and comfort operating in fast-moving AI infrastructure environments. It will be an added bonus if you have: Experience with GPU platforms and HPC primitives: InfiniBand/RDMA, topology-aware scheduling, high-throughput storage. Practical understanding of modern ML training stacks: PyTorch, DeepSpeed, FSDP/ZeRO, NCCL. Familiarity with efficiency and reliability metrics: Goodput, MFU, failure modes, preemption handling, health checks. Exposure to large-scale LLM training/inference systems. Experience in observability, performance tuning, or SRE/reliability engineering. Customer-facing technical experience (solutioning, support, architecture advisory). About Nebius: Nebius AI is an AI cloud platform with one of the largest GPU capacities in Europe. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment. Our Nebius HQ is in Amsterdam, with R&D hubs across Europe, the Middle East and North America. 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 it is like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership. Equal Opportunity Statement: Nebius is an equal opportunity employer and fosters an inclusive workplace. Applicants must be authorized to work in the country they apply.