Machine Learning Engineer, Assistant Quality
$180k - $205k • Remote • San Francisco, CA
Posted 9d ago
Job Location
San Francisco, CA
Tech Stack
Remote Work Policy
Fully remote
Categories
Machine Learning Engineer
About the job
Glean is seeking a Machine Learning Engineer to enhance the quality of its AI Assistant and autonomous agents. This role bridges production machine learning, LLM-powered systems, and product engineering, focusing on developing, evaluating, and refining assistant experiences for reliability and usefulness within enterprise workflows. You will tackle applied challenges in agent quality, evaluation, personalization, retrieval, and orchestration, with an emphasis on shipping production systems and improving the assistant's capabilities over time through enhanced signals, feedback loops, and end-to-end execution.
Responsibilities
- Build and improve ML and LLM-powered systems to enhance the quality of Glean's AI Assistant and autonomous agents.
- Design evaluation, benchmarking, and monitoring systems to measure assistant and model quality, as well as overall system performance.
- Develop and iterate on signals, prompts, workflows, and model-driven logic to improve reasoning, planning, personalization, and task completion.
- Apply RAG, semantic search, recommendation systems, post-training, reinforcement learning, and agent orchestration to improve product outcomes.
- Collaborate with product, design, and engineering teams to address customer needs and deliver high-quality production systems.
- Contribute to the data and ML infrastructure required for experimentation, evaluation, and continuous model improvement.
Requirements
- Experience building and improving ML and LLM-powered systems.
- Experience designing evaluation, benchmarking, and monitoring loops for AI systems.
- Experience developing signals, prompts, workflows, and model-driven logic.
- Experience working with RAG, semantic search, recommendation systems, post-training, reinforcement learning, or agent orchestration.
- Experience partnering with product, design, and engineering teams.
- Experience contributing to data and ML infrastructure for experimentation and evaluation.