Machine Learning Engineer, Assistant Quality

Glean Glean · Enterprise · San Francisco, CA · Engineering

Machine Learning Engineer to improve the quality of Glean's AI Assistant and autonomous agents, focusing on building, evaluating, and iterating on assistant experiences. The role involves applied problems across agent quality, evaluation, personalization, retrieval, and orchestration, with an emphasis on shipping production systems.

What you'd actually do

  1. Build and improve ML and LLM-powered systems that raise the quality of Glean’s AI Assistant and autonomous agents across real user workflows.
  2. Design evaluation, benchmarking, and monitoring loops to measure assistant quality, model quality, and end-to-end system performance.
  3. Develop and iterate on signals, prompts, workflows, and model-driven logic that improve reasoning, planning, personalization, and task completion quality.
  4. 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.
  5. Partner closely with product, design, and engineering teammates to understand customer pain points and ship high-quality production systems quickly.

Skills

Required

  • 2+ years of industry experience in machine learning, applied AI, or software engineering with significant ML ownership.
  • 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++.
  • A pragmatic, product-minded approach.
  • A proactive, low-ego working style and excitement about learning quickly in a high-velocity environment.

What the JD emphasized

  • shipping production systems
  • quality measurement
  • production iteration
  • evaluation
  • agent orchestration

Other signals

  • AI Assistant
  • autonomous agents
  • LLM-powered systems
  • evaluation
  • personalization
  • retrieval
  • orchestration