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Senior ML Engineer, Token Factory
Nebius GroupSenior ML Engineer developing fast, reliable, and effortless models for AI deployment. Involves enhancing methodologies for fine-tuning and inference optimization in Nebius Cloud's GPU environment.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in machine learning and reinforcement learning, with a strong focus on training large models using JAX and optimizing performance through advanced methodologies. Proficient in software engineering practices, including CI/CD and version control, to enhance model training and deployment efficiency.
Highest-signal resume keywords
Machine Learning ExpertiseReinforcement LearningJAX Framework ProficiencyLarge Model TrainingSoftware Engineering Skills
ATS Keywords
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Hard Skills
Machine LearningReinforcement LearningDeep LearningModel TrainingModel EvaluationPerformance OptimizationCI/CDVersion ControlUnit TestingLow-Precision Methodologies
Soft Skills
Strong CommunicationLeadership Abilities
Tools & Technologies
JAXPython
Industry Keywords
Large Language ModelsFine-Tuning MethodologiesInference BottlenecksScaling LawsSharding Strategies
Tech Stack
Tools & technologiesCloudPython
About the role
Key responsibilities & impact- Token Factory is a part of Nebius Cloud, one of the world’s largest GPU clouds, running tens of thousands of GPUs.
- We are building an inference & fine-tuning platform that makes every kind of foundation model — text, vision, audio, and emerging multimodal architectures — fast, reliable, and effortless to train & deploy at massive scale.
- Enhancing fine-tuning methodologies - both LoRA-based and full-parameter - for cutting-edge LLMs (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-4.7), focusing on both model quality and training efficiency.
- Identifying LLM inference bottlenecks to drive production speedups.
- Building model training and evaluation pipelines in JAX for speculative decoding, experimenting with architectures (dense/MoE, auto-regressive/parallel), and deriving scaling laws to guide resource allocation.
- Investigating low-precision (FP8, NVFP4/MXFP4) methodologies for supervised fine-tuning and reinforcement learning - spanning both inference and training - optimized for modern hardware
Requirements
What you’ll need- A profound understanding of theoretical foundations of machine learning and reinforcement learning.
- Deep expertise in modern deep learning for language processing and generation
- Experience with training large models on multiple computational nodes
- Reasonable understanding of performance aspects of large neural network training (sharding strategies, custom kernels, hardware features etc.)
- Strong software engineering skills (we mostly use Python)
- Deep experience with modern deep learning frameworks (we use JAX)
- Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing
- Strong communication and leadership abilities
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