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Salary: USD 200,000 - 280,000 / annual
Glean is a Work AI platform that helps enterprises work smarter with AI. The company has evolved from advanced enterprise search into a full-scale Work AI ecosystem, powering intelligent search, AI assistants, and scalable AI agents on a secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean enables organizations to govern, scale, and customize AI across their business without vendor lock-in.
The Enterprise Brain team is developing a suite of proactive AI products designed to revolutionize enterprise workflows by proactively detecting and automating tasks for users, unlocking true productivity. This work is built on deep user understanding and a state-of-the-art enterprise graph, combining LLM and advanced ML techniques, agent orchestration, and cutting-edge ranking methods.
In this role, you will work on deeply challenging ML problems involving user understanding and task prediction. You will invent new LLM workflows and signals to improve reasoning, planning, and personalization. You will design and optimize reinforcement learning and fine-tuning approaches to improve the quality of understanding, prediction, and other agentic systems. You will lead development of scalable evaluation, benchmarking, and optimization loops, and build and maintain robust ML pipelines for enterprise and knowledge graph construction. You will drive initiatives to measure, monitor, and improve data quality, model quality, and end-to-end system performance. You will collaborate with cross-functional teams to understand customer pain points and deliver high-quality, production-ready ML solutions. You will also mentor junior engineers or learn from experienced ones in a tight-knit, high-velocity environment.
The role is hybrid, requiring 4 days per week in the Mountain View office.
REQUIREMENTS:
- 6+ years of industry experience in machine learning engineering
- BA/BS in computer science, math, sciences, or a related field
- Proven ability to design, build, and ship production-ready models and systems
- Demonstrated expertise in ML evaluation, benchmarking, and data quality
- Proficiency in an ML framework such as TensorFlow or PyTorch
- Ability to thrive in a fast-moving, ambiguous environment, turning open-ended customer and ML problems into clear priorities and production-ready solutions
- Balance of speed with quality, making pragmatic tradeoffs to deliver measurable progress without losing sight of production readiness
