Description
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.
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).
- Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP).
- Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token.
- 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.
Requirements
- 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.
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