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Senior Applied Scientist, Efficient LLM Inference & Model OptimizationNebius · Palo Alto, California, United States
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Nebius

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Senior Applied Scientist, Efficient LLM Inference & Model Optimization

🗓️ Posted 2026-09-01 Palo Alto, California, United States Hybrid ICT
💰 Salary Not shared 📊 Market 15,000 EUR–25,000 EUR/mo

About the Company

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.

About 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.

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, KV

Requirements

  • ● 5+ years industry experience as an Applied Scientist, Researcher, or MLE.
  • ● Ability to execute and communicate clearly on multiple concurrent projects.
  • ● Strong Python abilities and a working knowledge of modern C++.
  • ● First-author publications at top-tier ML conferences (NeurIPS, ICML, ICLR, CVPR, ICCV, ACL, EMNLP, etc.).
  • ● Expertise with PyTorch, CUDA, Triton, ONNXRuntime, TensorRT, or other industry inference stack components.
  • ● A track record of shipping research as production code.