Senior Technical Product Manager - Serverless AI

Why work at Nebius
Nebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in-house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field.

Where we work
Headquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with R&D hubs across Europe, North America, and Israel. The team of over 1400 employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in-house AI R&D team.

The role   Nebius Serverless AI is our consumption-based compute platform for running AI workloads — training jobs, inference endpoints, and interactive development environments — without managing infrastructure. Users submit containerized workloads via CLI or UI, access GPU compute with pay-per-second billing, and the platform handles provisioning, lifecycle, and cleanup. We launched GA in Q1 2026 and are now scaling toward 1,000+ users while building the next generation of capabilities: autoscaling, multi-node distributed workloads, and developer-first tooling.
We are looking for a Senior Technical Product Manager to join the Serverless AI product team. Together you will divide ownership across the product surface — but individually, you will own your areas with full autonomy. This is not a role where you write requirements and hand them off. You will be the person who understands container runtimes, GPU scheduling, cold start optimization, and inference serving deeply enough to make correct technical trade-offs — and also the person who talks to customers, shapes the CLI experience, defines pricing, and drives adoption.
We are building the next generation of AI cloud — infrastructure designed from the ground up for GPU-intensive workloads, not retrofitted from legacy cloud. This is a lean, high-impact team where every person shapes the product directly. You need to be the kind of PM who amplifies engineering output by making the right calls on what to build and what to skip.
What success looks like in 12 months:

• Serverless AI has clear product-market fit with measurable activation and retention metrics improving quarter over quarter.
• Multi-node jobs and autoscaling endpoints are shipped and adopted by customers running production workloads.
• Cold start time is reduced from 1-3 minutes to under 60 seconds for common workloads through a combination of product and infrastructure improvements you drove.
• Developer experience (CLI, docs, error messages, onboarding flow) sets the standard that developers expect from a next-generation AI cloud.
• At least 3 product decisions you made are directly attributable to customer conversations or data analysis you conducted.

Your responsibilities will include:
1. Product Ownership

• 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.

2. Technical Depth:

• 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.

3. Customer & Market:

• 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