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Technical Product Manager - Soperator

Оценка ИИ

Отличная позиция в быстрорастущем AI-гиперскейлере с возможностью работать над передовыми технологиями (GPU, LLM) и влиять на open-source сообщество.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Высокая сложность обусловлена необходимостью глубоких технических знаний в области GPU-кластеров, Kubernetes и Slurm, а также опытом управления сложными инфраструктурными продуктами.

Анализ зарплаты

Медиана110 000 €
Рынок90 000 € – 140 000 €
Оценка ИИ

Зарплата в вакансии не указана, но для роли Technical Product Manager такого уровня в Амстердаме или Берлине рыночные предложения обычно начинаются от 90 000 евро в год плюс опционы. Nebius, как Nasdaq-компания, вероятно, предлагает конкурентоспособный пакет, соответствующий верхнему децилю рынка.

Сопроводительное письмо

I am writing to express my strong interest in the Technical Product Manager position for Soperator at Nebius. With a solid background in distributed systems and MLOps, I have closely followed Nebius's rapid ascent as a leading AI cloud provider. My experience in managing complex infrastructure products aligns perfectly with your mission to provide high-end, training-optimized infrastructure for the global AI economy.

In my previous roles, I have successfully led the delivery of technically demanding platforms, bridging the gap between deep engineering requirements and user-centric product design. I am particularly excited about the opportunity to own the Soperator roadmap, as I have hands-on experience with Kubernetes and Slurm, and I understand the critical importance of topology-aware scheduling and reliability in large-scale GPU clusters. I am eager to bring my expertise in product discovery and open-source strategy to help Nebius scale its next-generation AI compute platform.

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Откликнитесь в nebius уже сейчас

Присоединяйтесь к Nebius, чтобы определять будущее инфраструктуры для обучения крупнейших нейросетей в мире!

Описание вакансии

Why work at NebiusNebius 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 workHeadquartered 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

At Nebius, we’re building a next-generation AI compute platform for large-scale ML training and inference — from a few nodes to thousands of GPUs.

We’re 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.

If you care about systems that combine performance, reliability, and developer experience at the frontier of AI infrastructure, this role is for you.

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 → solution design → delivery → 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. Launched in November 2023, the Nebius AI platform provides high-end, training-optimized infrastructure for AI practitioners. As an NVIDIA preferred cloud service provider, Nebius AI offers a variety of NVIDIA GPUs for training and inference, as well as a set of tools for efficient multi-node training. 

Nebius AI owns a data center in Finland, built from the ground up by the company’s R&D team and showcasing our commitment to sustainability. The data center is home to ISEG, the most powerful commercially available supercomputer in Europe and the 16th most powerful globally (Top 500 list, November 2023).  

Nebius’s headquarters are in Amsterdam, Netherlands, with teams working out of R&D hubs across Europe and the Middle East. 

Nebius AI is built with the talent of more than 500 highly skilled engineers with a proven track record in developing sophisticated cloud and ML solutions and designing cutting-edge hardware. This allows all the layers of the Nebius AI cloud – from hardware to UI – to be built in-house, distictly differentiating Nebius AI from the majority of specialized clouds: Nebius customers get a true hyperscaler-cloud experience tailored for AI practitioners. We’re growing and expanding our products every day. 

What we offer

  • Competitive salary and comprehensive benefits package.
  • Opportunities for professional growth within Nebius.
  • Flexible working arrangements.
  • A dynamic and collaborative work environment that values initiative and innovation.

We’re growing and expanding our products every day. If you’re up to the challenge and are excited about AI and ML as much as we are, join us!

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Навыки

  • Product Management
  • Kubernetes
  • Slurm
  • Distributed Systems
  • MLOps
  • GPU
  • Ray
  • PyTorch
  • DeepSpeed
  • Infiniband
  • RDMA
  • NCCL
  • Observability

Возможные вопросы на собеседовании

Проверка понимания специфики продукта Soperator и его места в экосистеме.

Как бы вы объяснили ценность использования Slurm поверх Kubernetes для команды исследователей ML?

Оценка навыков приоритизации в сложной технической среде.

Как вы будете расставлять приоритеты между запросами на новые фичи от крупных клиентов и задачами по обеспечению надежности (reliability) платформы?

Проверка опыта работы с открытым исходным кодом.

Каков ваш подход к управлению open-source стратегией продукта? Как соблюсти баланс между интересами сообщества и бизнес-целями компании?

Оценка технической глубины в области ML-инфраструктуры.

С какими основными проблемами производительности (например, NCCL, InfiniBand) сталкиваются пользователи при масштабировании обучения LLM на тысячи GPU?

Проверка навыков работы с данными и метриками.

Какие ключевые метрики успеха вы бы внедрили для Soperator, чтобы измерить эффективность работы GPU-кластера?

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nebius
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