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Senior Research Scientist – Architectures Research
Nebius Group. Develop new model architectures and methods in areas including efficient, sparse, and adaptive attention; long-context models and persistent memory; post-training transformation of pretrained models; selective computation and dynamic inference; and architectures for reasoning and continual adaptation .
Core Competencies
Role fitUse this summary to align your resume positioning with the role.
Demonstrates expertise in developing innovative model architectures and methods in machine learning, with a focus on transformers, attention mechanisms, and efficient inference. Proven ability to conduct original research, mentor teams, and communicate complex technical concepts effectively.
ATS Keywords
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Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Develop new model architectures and methods in areas including efficient, sparse, and adaptive attention; long-context models and persistent memory; post-training transformation of pretrained models; selective computation and dynamic inference; and 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 research stream's direction
Requirements
What you’ll need- 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
- Applicants must be authorized to work in the country in which they apply and must provide proof of employment eligibility as a condition of hire
Benefits
Comp & 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