Senior LLM/VLM Inference Scientist

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

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.