Senior Applied ML Engineer — Large-Scale Retrieval & Search

Nebius Group; United States; Remote; USD 180,000 - 260,000; Full time

Job summary

Nebius is seeking a Senior Applied ML Engineer to design, train, and deploy ML models powering a scalable agent-native search platform. You will work on embedding-based indexing, retrieval systems, and real-time data access within a 24x7 production environment.

Join a world-class team building cloud infrastructure for AI, collaborating across engineering to integrate ML models into production services and optimize latency, quality, and cost in large-scale deployments.

Qualifications

• 5+ years of experience in software engineering or applied ML.
• Strong programming skills in Python, Go, or C++.
• Proven experience deploying ML models in production systems.
• Hands-on experience with retrieval, ranking, recommendation or similar ML problems.
• Strong understanding of machine learning and modern deep learning techniques.
• Experience working with large-scale data systems and high-throughput environments.

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 and improve quality metrics for agent-native search and build evaluation pipelines
• Work on systems operating at very large scale, including high-throughput query workloads
• Collaborate closely with engineering teams to integrate ML models into production services
• Analyse performance trade-offs across latency, quality, and cost
• Experiment with and apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems
• Contribute to product and architectural decisions in a fast-moving environment

Skills

Python

Go

C++

ML deployment

Retrieval systems

Embeddings

Transformers

NLP

Job description

Nebius is seeking a Senior Applied ML Engineer to design, train, and deploy ML models powering a scalable agent-native search platform. You will work on embedding-based indexing, retrieval systems, and real-time data access within a 24x7 production environment.

Join a world-class team building cloud infrastructure for AI, collaborating across engineering to integrate ML models into production services and optimize latency, quality, and cost in large-scale deployments.