Senior ML Engineer (Token Factory)

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

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.

Some directions we currently working on and which you can be a part of:

Advanced Fine-Tuning: 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.

Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. This involves 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.

Low Precision Training & Inference: Investigating low-precision (FP8, NVFP4/MXFP4) methodologies for supervised fine-tuning and reinforcement learning - spanning both inference and training - optimized for modern hardware

We expect you to have:

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

Nice to have:

Previous experience working with language models or other similar NLP technologies.

Familiarity with important ideas in LLM space, such as MHA, RoPE, ZeRO/FSDP, Flash Attention, quantization

A track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment.

Strong engineering skills, including experience in developing large distributed systems or high-load web services.