Machine learning Engineer - Agentic Retrieval

Role overview

Responsibilities

• check_circle Designing and implementing scalable retrieval systems including vector search, hybrid search (keyword + embedding + reranking), and structured query planning.
• check_circle Designing and optimize Retrieval-Augmented Generation (RAG) pipelines for multi-step, tool-using AI agents.
• check_circle Developing ranking, relevance modeling, and evaluation frameworks to improve search quality and answer grounding.
• check_circle Building indexing pipelines that transform heterogeneous enterprise data into unified, retrieval-ready representation.
• check_circle Building entity extraction and NLP pipelines that support agentic reasoning over enterprise content.
• check_circle Partnering with product, infrastructure, and applied research teams to ship production- grade AI capabilities.

Basic qualifications

• Master’s degree or higher in Computer Science, Artificial Intelligence, Machine Learning, Distributed Systems, or a related field.
• 5+ years of experience in machine learning, search infrastructure, information retrieval, or distributed systems
• Strong hands-on experience building and operating large-scale search or data platforms in production environments.
• Have experience building or integrating RAG systems and LLM-based applications in production.
• Possess proficiency in one or more of: Python, Go, Java, C#, or C++.
• Solid understanding of information retrieval fundamentals (inverted index, ranking, embeddings, hybrid retrieval).
• Strong system design skills with the ability to reason about scale, reliability, latency, and cost trade-off

Benefits

• check_circle As part of our award-winning workplace culture and commitment to delivering happiness, our benefits program offers a variety of perks, benefits, and options to help employees maintain their physical, mental, emotional, and financial health; support work-life balance; and contribute to their community in meaningful ways.

About the company