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Senior Applied Scientist, Efficient LLM Inference & Model Optimization

Nebius· ML
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Hybrid (Amsterdam, Berlin, London, Poland, Prague, Tel Aviv, Zurich)full-timePosted 3w ago

About the role

Senior scientist role focused on turning frontier LLM and VLM inference bottlenecks into research questions, conducting rigorous experiments, and translating findings into production capabilities for efficient inference and model optimization.

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.

Qualifications

  • 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.

Skills

Required:PhD-level expertise in relevant fieldPublication record or equivalent research artifactsPythonPyTorchLLM/VLM inferenceExperiment design and analysis

Benefits

  • 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
Last updated Oct 7, 2026. Interested? Apply on the company site.
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