Senior Applied Scientist, Efficient LLM Inference & Model Optimization

Role description

Role Overview

Nebius is looking for a Senior Applied Scientist to lead research projects in efficient LLM and VLM inference. The role involves conducting rigorous experiments, translating findings into production capabilities, and collaborating with engineering teams to bring promising results into production.

What You Will Do

The responsibilities include leading research projects, developing and evaluating methods for efficient LLM and VLM inference, building prototypes using PyTorch and other tools, and collaborating with MLEs and platform engineers to turn them into production components.

Why It Might Be a Fit

The ideal candidate will have a strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas, and excellent written and verbal communication skills.

Requirements

• PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline
• 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
• 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

• 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