Senior Applied ML Engineer — Real-Time Retrieval & Search

Nebius Group; Zurich (KS); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Competitive compensation

Career growth opportunities

Flexible ownership

Collaborative culture

Impactful AI projects

International environment

Job summary

Nebius Group is hiring a Senior Applied ML Engineer to advance an agent-native search platform powering AI systems. You will design, train, and deploy ML models for retrieval, ranking, and real-time access to fresh information at scale.

The role focuses on embedding-based indexing, large-scale retrieval, and collaboration with cross-functional teams to deploy production-grade ML systems with measurable impact on latency, quality and cost.

Qualifications

• 5+ years in software engineering or applied ML.
• Strong programming in Python, Go, or C++.
• Hands-on with deploying ML models in production.
• Experience with retrieval, ranking, or similar ML problems.
• Familiarity with large-scale data systems and high-throughput environments.
• Ability to design evaluation frameworks and model metrics.

Responsibilities

• Design, train, and deploy ML models for retrieval, reranking, and search relevance in production.
• Build and optimise embedding-based indexing and large-scale retrieval systems.
• Develop models supporting crawling, data selection, and content understanding.
• Define quality metrics and build evaluation pipelines.
• Collaborate with engineering teams to integrate ML models into production services.
• Analyze performance trade-offs across latency, quality, and cost.
• Experiment with state-of-the-art techniques in search, retrieval, and LLM-integrated systems.
• Contribute to product and architectural decisions in a fast-moving environment.

Skills

ML engineering

Python

Go

C++

Production ML

Retrieval systems

Large-scale data

Evaluation metrics

Product focus

Distributed teams

Job description

Nebius Group is hiring a Senior Applied ML Engineer to advance an agent-native search platform powering AI systems. You will design, train, and deploy ML models for retrieval, ranking, and real-time access to fresh information at scale.

The role focuses on embedding-based indexing, large-scale retrieval, and collaboration with cross-functional teams to deploy production-grade ML systems with measurable impact on latency, quality and cost.