Note: The job is a remote job and is open to candidates in USA. Nebius is leading a new era in cloud infrastructure for the global AI economy, building a full-stack AI cloud platform. The Senior Technical Product Manager will own specific areas of the Serverless AI product, driving technical decisions and customer engagement to ensure the platform meets user needs and achieves product-market fit.
Responsibilities
- Co-own the Serverless AI product roadmap — Jobs, Endpoints, and DevPods — taking primary ownership of specific product areas while collaborating closely with the other PM on shared priorities and cross-cutting decisions
- Write detailed, technically precise PRDs that engineering teams can execute against. Our PRDs specify CLI syntax, API contracts, state machines, and billing models — not abstract feature descriptions
- Make build/buy/defer decisions on capabilities like autoscaling, multi-node orchestration, HTTPS termination, secret injection, and health checking based on customer signal and strategic priorities
- Understand the full workload lifecycle: container image pull → VM provisioning → GPU attachment → workload execution → cleanup — well enough to identify bottlenecks and propose solutions
- Evaluate technical trade-offs in areas like container cold start optimization (image caching, snapshot restore, warm pools), GPU scheduling and bin-packing, and storage mount performance
- Work directly with engineers on architecture decisions for distributed training support, endpoint autoscaling policies, and fault tolerance mechanisms
- Stay current on the fast-moving serverless GPU infrastructure space — new inference frameworks (vLLM, TensorRT-LLM, SGLang), container runtimes, orchestration approaches — and translate trends into product direction
- Run customer discovery and feedback sessions with ML engineers and platform teams at AI startups and enterprises. Turn qualitative insight into specific product actions
- Analyze usage data, activation funnels, and churn patterns to identify where users get stuck and what features drive retention
- Track market dynamics, emerging technologies, and industry trends to inform product strategy and ensure Nebius stays ahead of where the market is heading
- Define and iterate on pricing, packaging, and tier strategy for Serverless AI
- Own the technical content strategy: quickstart guides, tutorials, reference architectures, and example workloads that reduce time-to-first-job
- Partner with marketing on developer-focused campaigns, webinars, and conference presence
- Work with Solution Architects and Sales to qualify serverless-fit opportunities and support technical evaluations
Skills
- You have built, shipped, and iterated on infrastructure or platform products used by developers or ML engineers. Not consumer apps. Not dashboards. Infrastructure
- You understand containers at a practical level — Docker, image registries, container runtimes, resource limits, networking. You've debugged why a container won't start, why GPU isn't visible inside it, or why a mount isn't working
- You have working knowledge of GPU computing for AI/ML: what GPU types exist and when to use them, how training and inference workloads differ in resource requirements, what vLLM / TensorRT-LLM / Triton are and why they matter
- You can read a CLI reference and know if it's well-designed. You've shaped developer-facing APIs, CLIs, or SDKs
- You have run real customer discovery — not surveys, but technical conversations with engineers where you learned something that changed your product direction
- You have 3+ years of product management experience in cloud infrastructure, AI/ML platforms, or developer tools
- Experience at a serverless or GPU cloud company
- Hands-on ML engineering background — you've trained models, deployed inference endpoints, or built ML pipelines yourself
- Experience with Kubernetes for ML workloads (Kubeflow, KServe, Ray Serve) and understanding of why many ML teams want to avoid it
- Prior experience building a product from early stage to scale in a fast-growing market
- Background in systems engineering, distributed systems, or site reliability engineering
Benefits
- 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
Company Overview