Senior ML Engineer (Token Factory)

Why work at Nebius Nebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in-house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field. Where we work Headquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with RD hubs across Europe, North America, and Israel. The team of over 800 employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in-house AI RD team. 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. Strong engineering skills, including experience in developing large distributed systems or high-load web services. Open-source projects that showcase your engineering prowess Excellent command of the English language, alongside superior writing, articulation, and communication skills. What we offer Competitive salary and comprehensive benefits package. Opportunities for professional growth within Nebius. Flexible working arrangements. A dynamic and collaborative work environment that values initiative and innovation. We’re growing and expanding our products every day. If you’re up to the challenge and are excited about AI and ML as much as we are, join us! ... (truncated, view full listing at source)