Staff Machine Learning Engineer, LLM Inference Optimization

Role description

Role Overview

Nebius is building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment. The Staff Machine Learning Engineer will own the path from model artifact to fast, reliable, cost-efficient endpoint, working at the intersection of model internals, inference engines, serving architecture, benchmarking, and production deployment.

What You Will Do

Own a platform-level optimization pillar, create reusable evaluation and benchmarking frameworks, provide technical direction, optimize LLM and VLM endpoints, deploy and configure inference engines, build and productionize model-compression workflows, and implement or integrate various optimization techniques.

Why It Might Be a Fit

The ideal candidate will have strong Python and PyTorch engineering skills, hands-on experience with LLM and VLM inference systems, and a strong understanding of transformer inference bottlenecks. They will also have the ability to reason quantitatively about latency, throughput, quality, utilization, and cost tradeoffs, and strong communication skills to collaborate with research, kernel, infrastructure, product, and customer teams.

Requirements

• Strong Python and PyTorch engineering skills
• Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference systems
• Practical knowledge of at least one modern inference stack
• Strong understanding of transformer inference bottlenecks
• Ability to reason quantitatively about latency, throughput, quality, utilization, and cost tradeoffs
• Strong communication skills and ability to collaborate with research, kernel, infrastructure, product, and customer teams

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