Senior Research Scientist (Architectures Research)

Description

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.

Responsibilities

• 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
• 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

• 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