Senior Applied Scientist, Efficient LLM Inference & Model Optimization

About this 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:

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Own focused research projects from hypothesis through experiment, ablation, prototype, and production handoff.

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Prepare internal reports, technical blogs, or papers when the work is externally credible.

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Partner directly with MLEs to ensure research prototypes become usable production components.

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Define and execute research programs in efficient LLM and VLM inference with measurable production impact.

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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.

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Build high-quality prototypes in PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, then work with MLEs and platform engineers to productionize them.

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Design rigorous evaluation methodology covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token.

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Publish papers, technical reports, blog posts, and open-source artifacts that build external credibility for Nebius Token Factory.

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Collaborate with MLE, GPU kernel, backend infrastructure, product, and customer teams to choose high-leverage research bets.

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Mentor engineers and scientists on experimental design, scientific rigor, and model/system tradeoffs.

Must-haves:

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