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Senior ML Engineer (Token Factory)
Исключительная вакансия в одной из самых быстрорастущих ИИ-инфраструктурных компаний. Работа с передовым стеком (JAX, новейшие GPU) и масштабными задачами делает эту позицию крайне привлекательной для топовых инженеров.
Сложность вакансии
Высокая сложность обусловлена требованиями к глубоким знаниям JAX, распределенного обучения на множестве узлов и оптимизации инференса (FP8, квантование). Роль требует сочетания сильной математической базы и навыков разработки высоконагруженных систем.
Анализ зарплаты
Зарплата в объявлении не указана, но для уровня Senior ML в Европе и Израиле в компаниях уровня Nebius рыночные вилки обычно начинаются от 90-100к евро. С учетом Nasdaq-листинга возможны значительные бонусы в виде акций (RSU).
Сопроводительное письмо
I am writing to express my strong interest in the Senior ML Engineer position within the Token Factory team at Nebius. With a deep background in training large-scale models and a solid command of JAX, I am particularly drawn to your work on inference optimization and low-precision training. My experience in distributed systems and fine-tuning methodologies aligns perfectly with your mission to make foundation models fast and reliable at a massive scale.
In my previous roles, I have successfully navigated the complexities of sharding strategies and performance optimization for high-load ML services. I am excited by the prospect of applying these skills to Nebius's cutting-edge GPU infrastructure, specifically in areas like speculative decoding and FP8/NVFP4 investigations. I thrive in dynamic, startup-like environments and look forward to contributing to the evolution of your inference and fine-tuning platform.
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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
Token Factory is a part of Nebius Cloud, one of the world’s largest GPU clouds, running tens of thousands of GPUs. We are building an inference & fine-tuning platform that makes every kind of foundation model — text, vision, audio, and emerging multimodal architectures — fast, reliable, and effortless to train & deploy at massive scale.
Some directions we currently working on and which you can be a part of:
- Advanced Fine-Tuning: Enhancing fine-tuning methodologies - both LoRA-based and full-parameter - for cutting-edge LLMs (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-4.7), focusing on both model quality and training efficiency.
- Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. This involves building model training and evaluation pipelines in JAX for speculative decoding, experimenting with architectures (dense/MoE, auto-regressive/parallel), and deriving scaling laws to guide resource allocation.
- LowPrecision Training & Inference: Investigating low-precision (FP8, NVFP4/MXFP4) methodologies for supervised fine-tuning and reinforcement learning - spanning both inference and training - optimized for modern hardware
We expect you to have:
- A profound understanding of theoretical foundations of machine learning and reinforcement learning.
- Deep expertise in modern deep learning for language processing and generation
- Experience with training large models on multiple computational nodes
- Reasonable understanding of performance aspects of large neural network training (sharding strategies, custom kernels, hardware features etc.)
- Strong software engineering skills (we mostly use Python)
- Deep experience with modern deep learning frameworks (we use JAX)
- Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing
- Strong communication and leadership abilities
Nice to have:
- Previous experience working with language models or other similar NLP technologies.
- Familiarity with important ideas in LLM space, such as MHA, RoPE, ZeRO/FSDP, Flash Attention, quantization
- A track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment.
- Strong engineering skills, including experience in developing large distributed systems or high-load web services.
- Open-source projects that showcase your engineering prowess
- Excellent command of the English language, alongside superior writing, articulation, and communication skills.
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
- Python
- JAX
- Machine Learning
- Reinforcement Learning
- LLM
- NLP
- CI/CD
- Distributed Systems
- Quantization
- Flash Attention
- LoRA
Возможные вопросы на собеседовании
Проверка понимания специфики фреймворка, используемого в компании.
В чем основные преимущества и сложности использования JAX по сравнению с PyTorch при обучении LLM на больших кластерах?
Оценка навыков оптимизации производительности.
Какие стратегии шардирования (ZeRO, FSDP, TP, PP) вы бы выбрали для обучения модели с 70B+ параметрами и почему?
Проверка знаний в области современных методов ускорения инференса.
Расскажите о механизме Speculative Decoding. Какие требования он предъявляет к архитектуре 'черновика' (draft model)?
Оценка опыта работы с низкоуровневой оптимизацией.
С какими проблемами стабильности обучения вы сталкивались при переходе на FP8 и как их решали?
Проверка навыков системного проектирования.
Как бы вы спроектировали систему оценки качества (evaluation pipeline) для LLM, которая должна работать в CI/CD цикле?
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