Senior Applied Scientist, Efficient LLM Inference & Model Optimization
# Senior Applied Scientist, Efficient LLM Inference & Model Optimization **Nebius** · Berlin · `On-site` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Arbeitnow (Германия и EU)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[ML]` --- ### 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, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA , or PyTorch internals. - Experience with post-training, SFT , DPO , RLHF , RLAIF , preference optimization, or synthetic data generation when connected to inference quality or efficiency. - Open-source research artifacts, widely used benchmarks, high-quality technical blogs, or invited talks in efficient AI systems. Benefits & Perks: - 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 What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know. Find Jobs in Germany on Arbeitnow
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Наблюдалась 2026-09-30, впервые 2026-09-30, источник — Arbeitnow (Германия и EU).