Senior Applied Scientist, Efficient LLM Inference & Model Optimization

FDE Times; Abu Dhabi; On-site; AED 717,000 - 963,000; Full time

Benefits offered by this job

Remote work reimbursement

Competitive compensation

Job summary

Nebius is leading a new era in cloud infrastructure for the global AI economy, building a full-stack AI cloud platform. You will own focused research on efficient LLM and VLM inference, conduct rigorous experiments, and translate findings into prototypes and production components used by engineers.

This role demands deep knowledge of quantization, distillation, and model/runtime co-optimization, plus strong Python and PyTorch implementation skills.

Qualifications

• PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or closely related discipline.
• Strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas.
• Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes.
• Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs.
• Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis.
• Excellent written and verbal communication.

Responsibilities

• Lead research projects in efficient LLM and VLM inference, 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 prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production 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 evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets.
• Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact.
• Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs.

Skills

Python

PyTorch

LLMs

VLMs

Quantization

Distillation

Experimental Design

Education

PhD in CS/ML/ML Systems

Tools

Triton

CUDA

PyTorch Internals

Job description

Job Description

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 is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build 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

• Lead research projects in efficient LLM and VLM inference, 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 prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production 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 evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets.
• Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact.
• Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs.

Must-haves

• A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline.
• A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas.
• Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes.
• Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs.
• Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis.
• Excellent written and verbal communication.

Nice-to-haves

• First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA.
• 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 applying post-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 talks demonstrating contributions to efficient 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.

Key employee benefits in the US

• Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families.
• 401(k) plan: Up to 4% company match with immediate vesting.
• Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
• Remote work reimbursement: Up to $85/month for mobile and internet.
• Disability & life insurance: Company-paid short-term, long-term and life insurance coverage.

Pay Transparency

We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law.

Base Compensation Range

$195,200—$262,200 USD

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.