Senior Applied Scientist, Efficient LLM Inference & Model Optimization

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 Factoryis looking forscientists who can turn frontierLLM and VLMinference bottlenecks into researchquestions, conductrigorous experiments, andtranslate their findings into production capabilities. You will own focusedresearch and optimizationprojects, write strong code, and produce prototypes, publications, and technical artifactsthat engineers canbuild on.

Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production.

Your responsibilities

Leadresearch projects in efficient LLM and VLMinference, from hypotheses and experiments through ablations, prototypes, and production handoff.

Develop and evaluate methods spanning quantization, quantization-aware training (QAT),distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization.

Build prototypesusingPyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks,and collaboratewith MLEs and platform engineers toturn them intoproduction components.

Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill-decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost.

Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices.

Develop rigorous evaluationscovering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token.Compare serving architectures under consistent workload conditions and GPU budgets.

Workwith MLE, GPU kernel, backend infrastructure, product, and customer teams toselect research priorities with measurable production impact.

Share results throughinternal reports, technical blogs, papers, and open-sourceartifacts, and mentorengineers and scientists on experimental design, scientific rigor, and model/systemtrade-offs.

Must-haves

APhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, appliedmathematics,or a closely relateddiscipline.

A strongpublication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas.

Strong Python andPyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes.

Deepknowledgeof LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-servingtrade-offs.

Strong experimental designskills coveringablations, baselines, metrics, statistical reasoning, and failure analysis.

Excellent written and verbal communication.

Nice-to-haves

First-author publicationsat venues such asNeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, orHPCA.

Experience deploying ML models or inference optimizations in production.

Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals.

Experienceapplyingpost-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency.

Open-source research artifacts, widely used benchmarks, technical blogs, or invited talksdemonstrating contributions toefficient AI systems.

Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP.

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:

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.

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