Senior Technical Product Manager, Token Factory

Nebius is leading a new era in cloud infrastructure for the global AI economy, building a full-stack AI cloud platform. As a Senior Technical Product Manager, you will own the product roadmap and drive the development and adoption of AI inference platform features.

Responsibilities:

• Own the product roadmap, delivery and developer experience for one of our flagship product direction - Dedicated Endpoints for AI Models Inference
• Partner with engineering and research to design tools that let customers run large-scale, high-load inference workloads efficiently
• Support sales & customers by preparing materials, leading PoCs/betas, and ensuring smooth onboarding of complex workloads
• Drive product–market fit through continuous user research, customer interviews, and market & competitive analysis
• Manage technical partnerships and ensure integrations deliver measurable value despite limited resources

Requirements:

• 3–5 years of product management experience, shipping technical products with 10–15+ cross-functional stakeholders (engineering, research, design, support, marketing, legal, leadership)
• Hands-on mindset — a power user of your own product, comfortable diving into: AIOps lifecycle: inference, experimentation, evaluation, fine-tuning, deployment; Cloud-native and DevOps tooling; Distributed systems and production infrastructure
• Excellent execution and project management skills — roadmaps, milestones, ownership, Agile processes, sprints, and change management (without creating chaos)
• Outstanding communication — able to write crisp PRDs, RFCs, runbooks, and release notes; articulate constraints and trade-offs clearly to technical and non-technical teams
• Technical fluency — able to speak the same language as engineering, define precise requirements, understand constraints, and make sensible trade-offs grounded in reality
• B2B SaaS experience, ideally working with enterprise customers and technical buyer personas
• Customer-obsessed — proactive in uncovering needs, understanding customer AI strategy, and ensuring our product becomes essential in their stack
• Bias toward action — runs problems end-to-end without waiting for instruction, even with incomplete data