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Senior Applied Scientist, Efficient LLM Inference & Model Optimization

Nebius· ML
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Palo Alto, CA$195.2k - $262.2kfull-timePosted 2mo ago

About the role

Senior scientist role focused on researching and translating frontier LLM/VLM inference bottlenecks into production-ready capabilities. Responsibilities include leading research projects, prototyping, evaluating methods (quantization, distillation, speculative decoding, KV-cache optimization, MoE, co-optimization), and collaborating with engineering teams to deploy production components. Emphasis on rigorous experimentation, scalable inference architectures, and dissemination of results.

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 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 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 in relevant fieldPublication track recordPythonPyTorchQuantization / QATDistillationInference systems
Nice to have:MoE / WideEP

Benefits

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