Senior Applied Scientist, Efficient LLM Inference & Model Optimization

The role 

Nebius Token Factory needs scientists who can turn frontier inference bottlenecks into research problems, publish credible work, and then help ship the results into production. This is not a papers-only research role. The Applied Scientist is expected to design rigorous experiments, write strong code, collaborate with engineers, and convert research into deployed inference capabilities.

A Senior Applied Scientist owns well-scoped research and production optimization projects. They can publish or prepare high-quality technical work while also producing code, experiments, and prototypes that engineers can use.

Your responsibilities: 

• Own focused research projects from hypothesis through experiment, ablation, prototype, and production handoff.

• Prepare internal reports, technical blogs, or papers when the work is externally credible.

• Partner directly with MLEs to ensure research prototypes become usable production components.

• Define and execute research programs in efficient LLM and VLM inference with measurable production impact.

• Invent, evaluate, and productionize methods for quantization, QAT, distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization.

• Build high-quality prototypes in PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, then work with MLEs and platform engineers to productionize them.

• Design rigorous evaluation methodology covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token.

• Publish papers, technical reports, blog posts, and open-source artifacts that build external credibility for Nebius Token Factory.

• Collaborate with MLE, GPU kernel, backend infrastructure, product, and customer teams to choose high-leverage research bets.

• Mentor engineers and scientists on experimental design, scientific rigor, and model/system tradeoffs.

Must-haves: 

• PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied math, or a closely related field.

• Strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas.

• Strong hands-on coding ability in Python and PyTorch; ability to move from idea to experiment to prototype quickly.

• Deep understanding of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving tradeoffs.

• Strong experimental design skills, including ablations, baselines, metrics, statistical reasoning, and failure analysis.

• Excellent written and verbal communication.

Nice-to-haves: 

• First-author publications in NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, HPCA, or comparable venues.

• 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 with post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation when connected to inference quality or efficiency.

• Open-source research artifacts, widely used benchmarks, high-quality technical blogs, or invited talks in efficient AI systems.