Senior ML Engineer – Token Factory

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🕒 July 1

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Nebius Group

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

🏢 Enterprise

☁️ SaaS

AI • Enterprise • SaaS

Nebius Group is building one of the world’s leading AI infrastructure companies, focusing on providing the necessary compute, storage, and tools for developers in the AI space. Based in Europe and listed on Nasdaq, Nebius has a global presence with R&D centers across Europe, North America, and Israel. The company's primary offering is an AI-centric cloud platform designed for intensive AI workloads, complemented by various other businesses involved in generative AI development, edtech, and autonomous technology.

📋 Description

• 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. • 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.

🎯 Requirements

• 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

🏖️ Benefits

• 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