Senior Applied Scientist, Efficient LLM Inference & Model Optimization

Status: Open
Remote policy: Not stated
Employment type: Not stated
Salary: Not stated
Categories: ML
Tech: python ml senior
Source: arbeitnow
First observed: 2026-09-30 16:37 UTC
Last seen: 2026-09-30 16:37 UTC
Source claims posted: 2026-09-30 15:55 UTC
Consecutive misses: 0 of 3

What the posting says

<div class="content-intro">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.</div><h3>The role </h3> 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:  <ul> <li> Own focused research projects from hypothesis through experiment, ablation, prototype, and production handoff. </li> <li> Prepare internal reports, technical blogs, or papers when the work is externally credible. </li> <li> Partner directly with MLEs to ensure research prototypes become usable production components. </li> <li> Define and execute research programs in efficient LLM and VLM inference with measurable production impact. </li> <li> 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. </li> <li> Build high-quality prototypes in PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, then work with MLEs and platform engineers to productionize them. </li> <li> Design rigorous evaluation methodology covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. </li> <li> Publish papers, technical reports, blog posts, and open-source artifacts that build external credibility for Nebius Token Factory. </li> <li> Collaborate with MLE, GPU kernel, backend infrastructure, product, and customer teams to choose high-leverage research bets. </li> <li> Mentor engineers and scientists on experimental design, scientific rigor, and model/system tradeoffs. </li> </ul> Must-haves:  <ul> <li> PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied math, or a closely related field. </li> <li> Strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. </li> <li> Strong hands-on coding ability in Python and PyTorch; ability to move from idea to experiment to prototype quickly. </li> <li> Deep understanding of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving tradeoffs. </li> <li> Strong experimental design skills, including ablations, baselines, metrics, statistical reasoning, and failure analysis. </li> <li> Excellent written and verbal communication. </li> </ul> Nice-to-haves:  <ul> <li> First-author publications in NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, HPCA, or comparable venues. </li> <li> Experience deploying ML models or inference optimizations in production. </li> <li> Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. </li> <li> Experience with post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation when connected to inference quality or efficiency. </li> <li> Open-source research artifacts, widely used benchmarks, high-quality technical blogs, or invited talks in efficient AI systems. </li> </ul>  <div class="content-conclusion">Benefits & Perks: <ul> <li>Competitive compensation</li> <li>Career growth and learning opportunities</li> <li>Flexibility and ownership</li> <li>Collaborative and innovative culture</li> <li>Opportunity to work on impactful AI projects</li> <li>International environment and talented teams</li> </ul> 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: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. 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.</div>

Find more English Speaking Jobs in United Kingdom on Arbeitnow

Quality

Completeness: 45%

x Salary range stated weight 35%
x Remote policy stated weight 20%
+ Location stated weight 15%
+ Organisation stated weight 15%
+ Publication date stated weight 15%

Not enough history yet to judge honesty signals.

Timeline

*
#1094425 2026-09-30 16:37 UTC

Published