Senior Research Scientist (Architectures Research)

Nebius

United Kingdom

Remote

Who this role is best for

Best suited to candidates with advanced research experience in machine learning, particularly in model architectures and attention mechanisms, working in the AI cloud infrastructure domain.

Best fit for

Candidates with a PhD and experience in transformer-based model architectures
— “A PhD or equivalent research experience in machine learning”
Researchers focused on efficient computation and inference optimization
— “Develop methods that preserve model quality while reducing training or inference cost”
Candidates with expertise in long-context modeling or memory systems
— “Experience with long-context modeling, memory systems, sparse or linear attention”
Researchers who can lead projects and communicate complex ideas clearly
— “Clear technical communication and the ability to lead research independently”

Things to consider

The role requires candidates to be authorized to work in the UK or Europe.
— “Applicants must be authorized to work in the country in which they apply”
The position involves publishing and open-source contributions as a key responsibility.
— “Publish research and contribute to open-source models, methods, and tools”

How to stand out

Highlight experience in model distillation and efficient inference in your resume and interview.
— “Experience with ... efficient inference is particularly relevant”
Emphasize your ability to independently lead research and define experimental programs.
— “Formulate original research questions and translate them into rigorous experimental programs”
Showcase your track record in publishing high-impact research in AI/ML.
— “A strong publication record or comparable evidence of original research”
Demonstrate your ability to collaborate with engineering teams on large-scale implementations.
— “Collaborate with engineering teams to validate ideas in efficient implementations”
Mention specific projects involving transformer architectures and attention mechanisms.
— “Deep knowledge of transformers, attention, language-model training, and modern model architectures”

Pace · SteadyCollaboration · MediumAutonomy · HighDecision Impact · CompanyLevel · Senior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

Develop new model architectures and methods
Collaborate with engineering teams to validate ideas in efficient implementations
Publish research and contribute to open-source models, methods, and tools

Typical background

PhD or equivalent research experience in machine learningDeep knowledge of transformers, attention, language-model training, and modern model architectures

Skills & requirements

Required

Model Architectures And MethodsEfficient, Sparse, And Adaptive AttentionLong-context Models And Persistent MemoryPost-training Transformation Of Pretrained ModelsSelective Computation And Dynamic InferenceNew Architectures For Reasoning And Continual AdaptationFormulate Original Research QuestionsDesign And Evaluate Architectural ChangesPreserve Model Quality While Reducing Training Or Inference CostCollaborate With Engineering TeamsPublish ResearchMentor ResearchersDeep Knowledge Of Transformers, Attention, Language-model Training, And Modern Model ArchitecturesStrong Publication Record Or Comparable Evidence Of Original ResearchStrong Implementation Skills In Python And A Modern Deep-learning FrameworkExperience Training Or Evaluating Models At ScaleClear Technical Communication

Preferred

Long-context ModelingMemory SystemsSparse Or Linear AttentionModel DistillationDistributed TrainingEfficient Inference

Stack & domain

Machine LearningTransformersAttentionLanguage-model TrainingModern Model ArchitecturesEfficient, Sparse, And Adaptive AttentionLong-context Models And Persistent MemoryPost-training Transformation Of Pretrained ModelsSelective Computation And Dynamic InferenceNew Architectures For Reasoning And Continual AdaptationPythonDeep-learning FrameworkTraining Or Evaluating Models At ScaleResearchCommunicationCollaborationLeadershipProblem-solvingAIMLCloud Infrastructure

About the role

Original posting from Nebius

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.

Source: Nebius careers