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 a high-performance inference and fine-tuning platform designed to push foundation models to their hardware limits. Our mission is to maximize throughput, minimise latency, and optimise cost-per-token across tens of thousands of GPUs.

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

Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. Squeezing the maximum performance for a wide range of LLM architectures at scale (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-5).

Inference engines support: Implement novel speculative decoding architectures, optimise components of various LLM designs (dense/MoE, autoregressive/parallel), and contribute to open-source inference engines.

Low Precision Training & Inference: Design and productionise low-precision (FP8, NVFP4/MXFP4) training and inference pipelines with measurable gains in throughput and cost-efficiency.

We expect you to have:

A profound understanding of theoretical foundations of machine learning and transformer architecture.

Experience profiling GPU workloads using Nsight, PyTorch profiler, or similar tools

Understanding of GPU memory hierarchy and compute/memory tradeoffs

Familiarity with important ideas in LLM space, such as MHA, RoPE, KV-cache, Flash Attention, and quantisation

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

Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing

Strong communication and leadership abilities

Nice to have:

Experience working with open-source inference engines (vLLM, SGLang, TensorRT-LLM), including contributions

Experience with kernel languages or DSLs such as Triton, Cute, CUTLASS, CUDA

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