About Nebius:
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.
Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.
The role
Nebius AI R&D conducts frontier applied research to make open-source AI highly competitive for real-world use cases. Our Architectures Research stream explores how models can attend, remember, reason, and adapt more effectively, enabling longer and richer workflows at lower computational cost.
We are looking for a Senior Research Scientist to develop new model architectures and methods in areas such as:
- Efficient, sparse, and adaptive attention
- Long-context models and persistent memory
- Post-training transformation of pretrained models -
- Selective computation and dynamic inference
- New architectures for reasoning and continual adaptation
Responsibilities
- Formulate original research questions and translate them into rigorous experimental programs
- Design and evaluate architectural changes at meaningful model scales
- Develop methods that preserve model quality while reducing training or inference cost
- Collaborate with engineering teams to validate ideas in efficient implementations
- Publish research and contribute to open-source models, methods, and tools
- Mentor researchers and help shape the stream's research direction
What we expect
- A PhD or equivalent research experience in machine learning
- Deep knowledge of transformers, attention, language-model training, and modern model architectures
- A strong publication record or comparable evidence of original research
- Experience designing rigorous experiments and drawing clear conclusions from ambiguous results
- Strong implementation skills in Python and a modern deep-learning framework
- Experience training or evaluating models at scale
- Clear technical communication and the ability to lead research independently
Experience with long-context modeling, memory systems, sparse or linear attention, model distillation, distributed training, or efficient inference is particularly relevant.
Benefits & Perks:
- 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
What's it like to work at Nebius:
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI
Equal Opportunity Statement:
Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.
Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.
If you need accommodations during the application process, please let us know.
Skills Required
- PhD or equivalent research experience in machine learning
- Deep knowledge of transformers, attention, language-model training, and modern model architectures
- Strong publication record or comparable evidence of original research
- Experience designing rigorous experiments and drawing clear conclusions from ambiguous results
- Strong implementation skills in Python and a modern deep-learning framework
- Experience training or evaluating models at scale
- Clear technical communication and the ability to lead research independently
- Experience with long-context modeling, memory systems, sparse or linear attention, model distillation, distributed training, or efficient inference
What We Do
Cloud platform specifically designed to train AI models
