Tech Lead Manager (AI Evaluations)

Join Glean as a Tech Lead Manager (AI Evaluations) and lead a team of applied AI scientists and engineers in building the evaluation strategy and platform for AI products. You will define success, build the evaluation platform, and integrate results into product decisions. You will also create and maintain evaluation sets, define a quality measurement system, and integrate evaluation into development and operations. Additionally, you will build and lead a high-performing team and partner closely with other teams to align evaluation standards.

- Lead a team of applied AI scientists and engineers to build the evaluation strategy and platform for AI products. - Define success metrics, build the evaluation platform, and integrate results into release and product decisions. - Develop a balanced quality measurement system for AI products, including correctness, groundedness, retrieval and context relevance, and user preference.

- Understanding of agent evaluation, including tool calling, multi-turn behavior, trajectory quality, task completion, and recovery from errors; experience with safety testing or red teaming is a plus - MS/PhD in Computer Science, AI or a related field - Strong coding ability in Python or another production language, with sound engineering fundamentals in testing, APIs, data modeling, and reliable distributed systems - Knowledge of public benchmarks across domains and capabilities is a plus - Experience building scalable data and developer platforms, experiment pipelines, or CI/CD integrations; familiarity with cloud infrastructure, Kubernetes, data stores, and observability is beneficial - Familiarity with evaluation methods and tradeoffs: curated and production-derived datasets, benchmark creation, deterministic metrics, rubric-based and pairwise judgments, human review, judge validation and calibration, human-model alignment, and statistical analysis of results - Hands-on experience designing, building, or operating evaluation systems for LLM/GenAI products, including model-based evaluators or autograders; practical understanding of how to assess model, retrieval/RAG, assistant, or agent behavior - 5+ years of engineering or applied AI team management experience, with a track record of technical leadership, developing engineers and scientists, and delivering cross-functional projects - 10+ years of experience in software engineering, ML infrastructure, data systems, or a related technical field. Experience must include building and operating production systems - Product-minded and collaborative: able to translate ambiguous quality goals into clear measures, communicate findings to technical and non-technical audiences, and influence roadmaps across teams - Strong judgment about measurement quality, including dataset representativeness, judge calibration, reproducibility, uncertainty, and the risk of optimizing to a narrow metric