Tech Lead Manager, AI Evaluations

The bottleneck in building reliable and useful AI systems isnt writing code anymore. Its knowing what good looks like applying that knowledge and experience from production usage into building rigorous evals and using those evals to guide continuous product self-improvement loops.

As the Tech Lead Manager of AI Evaluations you will lead a team of applied AI scientists and engineers and build the evaluation strategy and platform that helps Glean ship delightful and reliable AI experiences. You will make evaluation a continuous part of how we develop and operate models harnesses assistants agents and other AI products at Glean.

Youll guide the team in translating emerging AI/ML research into reliable evaluation systems build scalable efficient and reusable eval infrastructure and trusted quality signals and work across R&D product and marketing teams to turn evaluation results into better products and customer outcomes.

• Own the AI evaluation eng roadmap and technical direction end to end: from defining what success means and building the evaluation platform to integrating results into release and product decisions.
• Build scalable reusable evaluation infrastructure for datasets experiments graders trace capture and replay result analysis and dashboards. Make evaluations reproducible versioned cost-aware and easy for product and engineering teams to use.
• Create and maintain representative evaluation sets from expert-curated examples appropriately governed production signals synthetic cases and historical failures. Cover common workflows edge cases long-tail requests adversarial inputs and important user and domain slices; protect held-out acceptance sets from tuning leakage.
• Define a balanced quality measurement system for AI products including correctness groundedness retrieval and context relevance completeness instruction following citation quality user preference and task success. For agents evaluate tool selection and arguments multi-step trajectories policy compliance recovery and final task state not just the final response.
• Develop a fit-for-purpose mix of deterministic checks model-based graders (including LLM-based autograders) and human evaluation. Establish clear rubrics; validate and calibrate automated judges against human judgments; and measure evaluator reliability and limitations so teams can trust and interpret scores.
• Integrate evaluation into development and operations: fast CI regression checks broader pre-release suites model and prompt comparisons and sampled production evaluation. Build feedback loops from user feedback and production traces into new test cases and use monitoring canaries and experiments to detect regressions and drift.
• Make quality tradeoffs measurable by connecting evaluation outcomes to latency reliability and cost. Help teams diagnose failures by slice and trace prioritize improvements and make evidence-based decisions about models routing prompts and system changes.
• Build and lead a high-performing team of applied AI scientists and engineers; coach and develop team members. Partner closely with product infrastructure security and application teams to align evaluation standards and deliver the highest-impact work.

• 10 years of experience in software engineering ML infrastructure data systems or a related technical field. Experience must include building and operating production systems.
• 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.
• 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.
• 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.
• 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.
• Strong coding ability in Python or another production language with sound engineering fundamentals in testing APIs data modeling and reliable distributed systems.
• Experience building scalable data and developer platforms experiment pipelines or CI/CD integrations; familiarity with cloud infrastructure Kubernetes data stores and observability is beneficial.
• Strong judgment about measurement quality including dataset representativeness judge calibration reproducibility uncertainty and the risk of optimizing to a narrow metric.
• Knowledge of public benchmarks across domains and capabilities is a plus.
• 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.
• MS/PhD in Computer Science AI or a related field.