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Glean is a Work AI platform that helps enterprises work smarter with AI. The company has evolved from advanced enterprise search into a full-scale AI ecosystem, powering intelligent search, AI assistants, and scalable AI agents on a secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean enables organizations to govern, scale, and customize AI across their business without vendor lock-in.
The Machine Learning Engineer role focuses on improving the quality of Glean's AI Assistant and autonomous agents. This position sits at the intersection of production machine learning, LLM-powered systems, and product engineering, with emphasis on building, evaluating, and iterating on assistant experiences that are useful, reliable, and grounded in real enterprise workflows.
Key responsibilities include:
- Building and improving ML and LLM-powered systems that raise the quality of Glean's AI Assistant and autonomous agents across real user workflows
- Designing evaluation, benchmarking, and monitoring loops to measure assistant quality, model quality, and end-to-end system performance
- Developing and iterating on signals, prompts, workflows, and model-driven logic that improve reasoning, planning, personalization, and task completion quality
- Working across areas such as RAG, semantic search, recommendation-style systems, post-training or reinforcement learning, and agent orchestration where they materially improve product outcomes
The ideal candidate is excited by shipping production systems rather than pure research, and wants to help shape how Glean's assistant improves over time through stronger signals, tighter feedback loops, and better end-to-end execution quality. This role offers the opportunity to work on applied problems across agent quality, evaluation, personalization, retrieval, and orchestration.
