Build and scale faster on the purpose-built AI cloud, engineered from silicon to API. Backed by Accel.
About the role
Nebius Token Factory is building an AI training and model post-training capability for frontier model improvement. This role owns the infrastructure that makes large-scale training and RL experiments possible, reliable, reproducible, and efficient. The work sits at the intersection of distributed systems, GPU performance, model training frameworks, RL pipelines, and production engineering.
What they're looking for
- Strong Python and PyTorch engineering skills, with the ability to move quickly from idea to experiment to working system
- Hands-on experience across at least two of: model training, post-training/ RL , applied modeling, data pipelines, or large-scale ML systems
- Ability to design rigorous experiments with baselines, ablations, metrics, and failure analysis
- Practical understanding of modern LLM behavior, instruction tuning, preference optimization, and evaluation challenges
- Practical understanding of transformer training bottlenecks, memory pressure, communication overhead, and checkpointing
- Ability to reason quantitatively about model quality, throughput, utilization, reliability, cost, and research velocity
More about this role
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.
Nebius Token Factory is building an AI training and model post-training capability for frontier model improvement. This role owns the infrastructure that makes large-scale training and RL experiments possible, reliable, reproducible, and efficient. The work sits at the intersection of distributed systems, GPU performance, model training frameworks, RL pipelines, and production engineering.
A Senior Machine Learning Engineer owns substantial ML work end to...
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