5 months ago
Remote, Worldwide +2 moreSenior
Responsibilities
- Design, train, and deploy machine learning models for retrieval, reranking, and search relevance in production.
- Build and optimize embedding-based indexing and large-scale retrieval systems.
- Develop models for crawling, data selection, and content understanding.
- Define and improve quality metrics and evaluation pipelines for agent-native search.
- Work on very large-scale systems with high-throughput query workloads.
- Collaborate with engineering teams to integrate machine learning models into production services.
- Analyze latency, quality, and cost trade-offs.
- Apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems.
- Contribute to product and architectural decisions.
Requirements
- At least 5 years of experience in software engineering or applied machine learning.
- Strong programming skills in Python, Go, or C++.
- Proven experience deploying machine learning models in production systems.
- Hands-on experience with retrieval, ranking, recommendation, or similar machine learning problems.
- Strong understanding of machine learning and modern deep learning techniques.
- Experience with large-scale data systems and high-throughput environments.
- Ability to design evaluation frameworks and define meaningful model metrics.
- Experience with search systems or large-scale information retrieval is preferred.
- Familiarity with embeddings, transformers, and modern NLP systems is preferred.
- Experience with LLM-powered or agent-based systems is preferred.
- Open-source contributions, technical publications, conference talks, or competitive ML experience such as Kaggle are preferred.
- Coding interviews are part of the hiring process.
Benefits
- Competitive salary and comprehensive benefits package.
- Flexible working arrangements.
- Opportunities for professional growth within Nebius.
- Dynamic and collaborative work environment.
Categories
About Nebius
The Nebius AI Cloud brings powerful full-stack infrastructure for AI developers and practitioners across startups, enterprises and science institutes to build and deploy generative AI applications and rapidly deliver scientific breakthroughs by training and running ML models within a secure, high-performance, and cost-optimized cloud environment.