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SeniorУдалённоПолная занятость

Sr. Solution Engineer/Architect (GPU Infrastructure)

Оценка ИИ

Исключительная возможность работать в быстрорастущей компании, котирующейся на Nasdaq, на переднем крае технологий ИИ. Высокий балл обусловлен работой с передовым оборудованием (Grace Blackwell) и глобальным масштабом проектов.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Высокая сложность обусловлена необходимостью глубоких знаний в области GPU-инфраструктуры (NVIDIA), сетевых технологий и системного администрирования Linux для работы с масштабными ИИ-кластерами. Роль требует сочетания экспертных технических навыков и умения эффективно взаимодействовать с клиентами в стрессовых ситуациях.

Анализ зарплаты

Медиана215 000 $
Рынок175 000 $ – 260 000 $
Оценка ИИ

Предлагаемая роль Senior-уровня в сфере ИИ-инфраструктуры в США обычно оплачивается выше среднего по рынку ИТ из-за дефицита специалистов по GPU. Ожидаемый диапазон для удаленной позиции в крупной международной компании составляет $180,000 – $250,000 в год плюс бонусы и опционы.

Сопроводительное письмо

I am writing to express my strong interest in the Sr. Solution Engineer/Architect position at Nebius. With extensive experience in high-performance computing and a deep understanding of NVIDIA-based GPU infrastructure, I have consistently helped organizations bridge the gap between complex hardware capabilities and successful AI workload deployments. My background in system-level troubleshooting and multi-node cluster optimization aligns perfectly with Nebius's mission to lead the global AI economy.

In my previous roles, I have acted as a primary technical interface for enterprise customers, translating their unique requirements into scalable architectures. I am particularly excited about the opportunity to work with next-generation platforms like Grace Blackwell and to contribute to the Neocloud infrastructure organization. I am confident that my proactive, ownership-driven approach and technical expertise in GPU-based systems will allow me to drive significant value for your customers and internal engineering teams alike.

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Откликнитесь в nebius уже сейчас

Присоединяйтесь к Nebius и станьте ключевым архитектором будущего ИИ-инфраструктуры на базе новейших GPU NVIDIA!

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.

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 800 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 team

You will join our Neocloud infrastructure organization supporting large-scale GPU clusters designed for AI training, inference, and high-performance computing (HPC) workloads. Our teams operate at the intersection of cloud infrastructure, hardware engineering, and customer-facing technical enablement, ensuring customers can deploy, scale, and optimize advanced AI workloads with confidence.

This is a highly collaborative environment where you will work closely with data center operations, networking, platform engineering, and product teams to deliver reliable, high-performance solutions and continuously improve the customer experience.

The role

As a Customer Solutions Engineer, you will act as the primary technical partner for customers deploying and operating GPU clusters and AI infrastructure on Nebius. You will bridge customer requirements with internal engineering capabilities, ensuring successful deployment, stable operations, and ongoing optimization of complex, high-performance environments.

This role requires a combination of strong customer engagement skills and deep technical expertise. You will work closely with customers throughout the lifecycle of their workloads—from initial deployment through scaling and optimization—with a focus on GPU-based systems and data center infrastructure.

Your responsibilities will include:

  • Acting as the main technical interface for customers running workloads on Nebius GPU infrastructure.
  • Supporting customers in deploying, configuring, and tuning GPU-based environments for performance and reliability.
  • Investigating and resolving complex issues spanning hardware, networking, operating systems, and cluster-level behavior.
  • Partnering with internal teams (data center operations, networking, platform engineering) to coordinate and drive resolution of customer-impacting issues.
  • Converting customer requirements into practical architectures, configurations, and execution plans.
  • Identifying opportunities to improve system performance, stability, and overall customer experience.
  • Developing and maintaining technical documentation, including solution patterns, troubleshooting guides, and operational best practices.
  • Contributing to continuous improvement by surfacing recurring issues, gaps, and optimization opportunities to internal teams.

We expect you to have:

  • Experience in a customer-facing technical role (e.g., solutions engineer, support engineer, technical account manager, or similar).
  • Strong understanding of GPU infrastructure, including NVIDIA-based systems, multi-node environments, and performance considerations.
  • Hands-on experience with Linux systems and system-level troubleshooting.
  • Familiarity with large-scale compute environments such as GPU clusters, AI infrastructure, or supercomputing systems.
  • Ability to diagnose issues across hardware, networking, and software layers.
  • Strong analytical and problem-solving skills, with the ability to operate effectively in high-pressure situations.
  • Excellent communication skills, with the ability to explain complex technical concepts clearly to customers.
  • A proactive, ownership-driven approach with a strong focus on customer success.

Nice to have:

  • Experience working with NVIDIA Grace Blackwell or similar next-generation GPU platforms.
  • Experience with cluster validation, benchmarking, or performance testing tools (e.g., HPL, NCCL)

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

  • GPU
  • NVIDIA
  • Linux
  • HPC
  • NCCL
  • HPL
  • Cloud Infrastructure
  • Networking
  • Troubleshooting
  • Architecture

Возможные вопросы на собеседовании

Проверка понимания специфики межсоединений в GPU-кластерах, что критично для обучения больших моделей.

Можете ли вы объяснить роль библиотеки NCCL в многоузловых GPU-системах и как вы будете диагностировать проблемы с пропускной способностью сети?

Оценка практического опыта оптимизации производительности.

Какие метрики и инструменты (например, HPL или специфические профилировщики NVIDIA) вы используете для валидации готовности кластера к промышленным нагрузкам ИИ?

Проверка навыков траблшутинга на стыке железа и софта.

Опишите ваш подход к расследованию инцидента, когда клиент сообщает о падении производительности обучения (MFU) на конкретном узле в кластере из 100+ GPU.

Оценка способности работать с клиентами и управлять ожиданиями.

Как вы объясните клиенту необходимость изменения их текущей архитектуры для поддержки масштабирования на новые платформы, такие как Grace Blackwell?

Проверка знаний системного уровня.

С какими наиболее сложными проблемами на уровне ядра Linux или драйверов GPU вы сталкивались при работе с HPC-нагрузками и как их решали?

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