Senior Applied Scientist, Efficient LLM Inference & Model Optimization

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About the role

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 Nebius Token Factory needs scientists who can turn frontier inference bottlenecks into research problems, publish credible work, and then help ship the results into production. This is not a papers-only research role. The Applied Scientist is expected to design rigorous experiments, write strong code, collaborate with engineers, and convert research into deployed inference capabilities. A Senior Applied Scientist owns well-scoped research and production optimization projects. They can publish or prepare high-quality technical work while also producing code, experiments, and prototypes that engineers can use. Your responsibilities : • Own focused research projects from hypothesis through experiment, ablation, prototype, and production handoff. • Prepare internal reports, technical blogs, or papers when the work is externally credible. • Partner directly with MLEs to ensure research prototypes become usable production components. • Define and execute research programs in efficient LLM and VLM inference with measurable production impact. • Invent, evaluate, and productionize methods for quantization, QAT , distillation, speculative decoding, KV -cache reuse, KV -cache compression, long-context inference, MoE routing, and model/runtime co-optimization. • Build high-quality prototypes in PyTorch, Triton, CUDA -adjacent tooling, or inference-serving frameworks, then work with MLEs and platform engineers to productionize them. • Design rigorous evaluation methodology covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. • Publish papers, technical reports, blog posts, and open-source artifacts that build external credibility for Nebius Token Factory. • Collaborate with MLE, GPU kernel, backend infrastructure, product, and customer teams to choose high-leverage research bets. • Mentor engineers and scientists on experimental design, scientific rigor, and model/system tradeoffs. Must-haves : • PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied math, or a closely related field. • Strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. • Strong hands-on coding ability in Python and PyTorch; ability to move from idea to experiment to prototype quickly. • Deep understanding of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving tradeoffs. • Strong experimental design skills, including ablations, baselines, metrics, statistical reasoning, and failure analysis. • Excellent written and verbal communication. Nice - to - have s : • First-author publications in NeurIPS, ICML , ICLR , MLSys, ACL , EMNLP , ASPLOS , OSDI , SOSP , ISCA , HPCA , or comparable venues. • Experience deploying ML models or inference optimizations in production. • Experience with vLLM, S

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