Technical product manager in AI compute platform

Nebius; United States; Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive compensation

Career growth and learning

International environment

Job summary

Nebius Group ищет опытного менеджера продукта для ведения полного цикла разработки и внедрения инфраструктурных платформ в облаке. Вы будете ответственны за стратегию, дорожную карту и метрики успеха, тесно сотрудничая с инженерными, SRE и бизнес-командами.

Требуется опыт в крупномасштабных инфраструктурных проектах и сильные аналитические навыки. Предлагается работа в международной среде с возможностями роста и гибкими условиями.

Qualifications

• 6+ лет в Product Management, Platform PM, Infrastructure PM или SRE/Engineering Lead
• Сильная техническая база и глубина облачной инфраструктуры
• Умение общаться с инженерами на уровне равного партнёра
• Опыт работы с облачными GPU/HPC инфраструктурами
• Доказанная история выпуска технически сложных платформ
• Сильная аналитика и работа с метриками

Responsibilities

• Владение end-to-end продуктовой ответственностью: стратегия, road map, discovery, delivery
• Проектирование контрактов платформы — API, семантика, системные события
• Координация кросс-функциональной работы по платформе
• Преобразование пользовательских болей в продуктовые обязательства
• Выработка и защита дизайна API и архитектурных решений
• Определение и отслеживание метрик успеха, взаимодействие со службой поддержки и орг. командами

Skills

Стратегическое мышление

Коммуникационные навыки

Принятие решений

Адаптивность

Tools

Kubernetes

Slurm

GPU инфраструктура

NVIDIA стек

NCCL

Job description

Описание

Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

Задачи

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) elevate to when a system behavior, API contract, or operational pattern needs a product decision, not a workaround.

Требования

• 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.
• Будет плюсом: 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)

Условия

• 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
• Fast moving
• Bold thinking
• Constant growth
• Meaningful impact
• Trust and real ownership
• Opportunity to shape the future of AI