Machine Learning Engineer, Assistant Quality

Build and improve ML and LLM-powered systems that raise the quality of Glean’s AI Assistant and autonomous agents across real user workflows.

Design evaluation, benchmarking, and monitoring loops to measure assistant quality, model quality, and end-to-end system performance.

Develop and iterate on signals, prompts, workflows, and model-driven logic that improve reasoning, planning, personalization, and task completion quality.

Work across areas such as RAG, semantic search, recommendation-style systems, post-training or reinforcement learning, and agent orchestration where they materially improve product outcomes.

Strong hands-on coding ability and a track record of shipping production systems, not just prototypes or research projects.

Experience in one or more of the following areas: LLM applications, NLP, search, retrieval, recommendations, evaluation frameworks, agent systems, or personalization.

Comfort working across both modeling and product engineering details, including experimentation, quality measurement, and production iteration.

Proficiency in common ML tooling and strong software engineering fundamentals in languages such as Python, Go, Java, or C++.