
Senior Research Scientist
Hover or tap a row for full statistics (EUR / month on this chart).
Salary analysis
Compared with the selected benchmark ("All roles in Remote - Europe"), this listing's salary midpoint is about 95% lower. The offer sits below the benchmark range (€1,895–€14,075). The listed pay band (€321–€500) is tighter than the benchmark, which suggests lower salary variability. This benchmark is based on 30 comparable listings.
| Market | Lower bound (25th percentile) | Median | Upper bound (75th percentile) |
|---|---|---|---|
| All roles in Remote - Europe | €1,895/per month | €4,927/per month | €14,075/per month |
| Pay in our data — not quoted in ad (Senior) | €321/per month | €411/per month | €500/per month |
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.
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