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ML Engineer - Life Sciences (Early Talent)
Оценка ИИОтличная возможность для старта карьеры в крупной международной компании с листингом на Nasdaq. Проект на стыке AI и Life Sciences предлагает уникальный опыт и потенциальный переход в штат.
Сложность вакансии
Оценка ИИПозиция ориентирована на начинающих специалистов, но требует глубоких знаний в области ML и Computer Science. Основная сложность заключается в специфике задач по оптимизации инференса и работе с тяжелыми биологическими моделями.
Анализ зарплаты
Оценка ИИЗарплата для стажеров и начинающих специалистов в Амстердаме в сфере ML обычно выше среднего по рынку стажировок. Nebius предлагает конкурентоспособную оплату, соответствующую уровню технологических хабов Европы.
Сопроводительное письмо
I am writing to express my strong interest in the ML Engineer - Life Sciences (Early Talent) position at Nebius. As a student with a deep focus on machine learning and a keen interest in the intersection of AI and biology, I am particularly drawn to your mission of making complex biological models more efficient and accessible. My background in Python and deep learning frameworks, combined with my passion for solving infrastructure bottlenecks, aligns perfectly with the goals of this project.
During my academic projects, I have developed a solid foundation in computer science and ML fundamentals. I am especially excited about the opportunity to work on model compression techniques like quantization and pruning to optimize protein folding and design models. The prospect of learning from the experienced R&D team at Nebius and contributing to real-world AI cloud infrastructure is incredibly motivating. I am confident that my problem-solving skills and willingness to learn will allow me to make a meaningful contribution to your team in Amsterdam.
Составьте идеальное письмо к вакансии с ИИ-агентом
Откликнитесь в nebius уже сейчас
Присоединяйтесь к Nebius, чтобы оптимизировать передовые биологические ИИ-модели и начать карьеру в одном из самых перспективных облачных провайдеров Европы!
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.
Summary:
Location: Amsterdam
Duration: 3-6 months
Start date: June 2026
Compensation: Paid
Eligibility: Current University student (Computer Science or related field), Recent Graduate or Early Career specialist
About the role
Biological AI models (protein folding, protein design, and large foundation models) are powerful but heavy and expensive to run. This project focuses on making them faster and more efficient at inference time without significantly hurting biological quality.
You will work on profiling bottlenecks, applying model compression and architectural optimizations, and building efficient inference pipelines. The goal is to make these models practical for real-world research and production use.
Your responsibilities will include:
- Profile inference bottlenecks in selected biological models
- Implement and test optimization techniques (quantization, pruning, distillation)
- Explore efficient attention and architecture-level improvements
- Build and benchmark optimized inference pipelines
- Evaluate speed, memory, and accuracy trade-offs
- Write clean, well-documented experimental code
- Share results and practical deployment recommendations
We expect you to have:
- Enrollment in or completion of a degree in computer science, AI, or a related field
- Strong knowledge of computer science and machine learning fundamentals
- Experience with Python and familiarity with deep learning frameworks
- Ability to write clean, efficient code
- Strong problem-solving skills and a willingness to learn quickly
- Interest in life sciences
Nice to have:
- Familiarity with large language models and transformer architectures
- Experience profiling GPU workloads and optimizing deep learning systems
- Experience with model compression techniques (quantization, pruning, distillation)
- Experience working with large-scale models or distributed inference
- Contributions to open-source ML projects
What we offer:
- Competitive salary and comprehensive benefits package
- Mentorship from experienced AI, ML, and cloud infrastructure professionals
- Hands-on experience with real customer workloads and production systems
- Opportunities for professional growth within Nebius
- A dynamic and collaborative work environment that values initiative and innovation
- Opportunity to be considered for a full-time role after the Early Talent Program
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!
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!
Создайте идеальное резюме с помощью ИИ-агента
Навыки
- Python
- Machine Learning
- Deep Learning
- Quantization
- Pruning
- Knowledge Distillation
- GPU Profiling
- Transformers
- Large Language Models
- Computer Science
Возможные вопросы на собеседовании
Проверка понимания ключевой задачи вакансии — оптимизации моделей для инференса.
Можете ли вы объяснить разницу между квантованием (quantization) и прунингом (pruning)? В каких случаях один метод предпочтительнее другого?
Вакансия связана с Life Sciences, важно понимать специфику данных.
Знакомы ли вы с архитектурами моделей для предсказания структуры белка (например, AlphaFold)? Какие вычислительные сложности возникают при их работе?
Работа подразумевает профилирование узких мест.
Какие инструменты вы бы использовали для профилирования нагрузки на GPU при выполнении инференса глубоких нейронных сетей?
Оценка навыков написания чистого и эффективного кода.
Как бы вы организовали пайплайн обработки данных для инференса крупной модели, чтобы минимизировать задержки (latency)?
Проверка мотивации и способности к быстрому обучению.
Расскажите о самом сложном техническом проекте, над которым вы работали. С какими трудностями вы столкнулись и как их решили?
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