Senior Applied Scientist (Efficient LLM Inference)

Location: Amsterdam, Netherlands; Berlin, Germany; London, United Kingdom; Poland; Prague, Czech Republic; Tel Aviv, Israel; or Zurich, Switzerland. Applicants must be authorized to work in the country where they apply.

Company

Nebius builds a full-stack AI cloud platform for data processing, model training, and production deployment, with infrastructure spanning compute, storage, networking, and applied AI.

What you will do

• Own research projects from hypothesis and experiment design through ablation, prototyping, and production handoff.
• Define and execute research programs in efficient LLM and VLM inference with measurable production impact.
• Develop and productionize methods for quantization, QAT, distillation, speculative decoding, KV-cache reuse and compression, long-context inference, MoE routing, and model/runtime co-optimization.
• Build prototypes with PyTorch, Triton, CUDA-adjacent tooling, and inference-serving frameworks.
• Evaluate quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token.
• Collaborate with MLE, GPU kernel, backend infrastructure, product, and customer teams; publish technical work and mentor engineers and scientists.

Requirements

• PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a related field.
• Strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, or serving systems.
• Strong hands-on coding skills in Python and PyTorch.
• Deep understanding of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving tradeoffs.
• Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis.
• Excellent written and verbal communication.

Nice to have

• First-author publications in leading ML, systems, architecture, or NLP 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 related to inference quality or efficiency.
• Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks in efficient AI systems.

Culture & Benefits

• Competitive compensation.
• Career growth and learning opportunities.
• Flexibility and ownership.
• Collaborative, innovative, and international working environment.
• Opportunity to work on impactful AI projects.