Machine Learning Engineer, Enterprise Brain

About the Role:

Glean is seeking a few Machine Learning engineers who want to focus on a combination of Quality and traditional ML work to help us build the Enterprise Brain. The Enterprise Brain team is developing a suite of proactive AI products that aims to revolutionize enterprise workflows by proactively detecting and automating tasks for users - thus unlocking true productivity. This is built on top of a deep user understanding and state of the art Enterprise graph. The project involves using both LLM and other advanced ML techniques, agent orchestration and cutting-edge ranking techniques.

You will:

• Work on deeply challenging ML problems involving user understanding and task prediction.
• Invent new LLM workflows and signals to improve reasoning, planning, and personalization.
• Design and optimize reinforcement learning and fine-tuning approaches to improve the quality of understanding, prediction and other agentic systems.
• Lead development of scalable evaluation, benchmarking, and optimization loops.
• Build and maintain robust ML pipelines for enterprise and knowledge graph construction.
• Drive initiatives to measure, monitor, and improve data quality, model quality, and end-to-end system performance.
• Collaborate with cross-functional teams to deeply understand customer pain points and deliver high-quality, production-ready ML solutions.
• Mentor junior engineers or learn from experienced ones in a tight-knit, high-velocity environment.

About you:

• You bring 6+ years of industry experience in machine learning engineering.
• You hold a BA/BS in computer science, math, sciences, or a related field.
• You have a proven ability to design, build, and ship production-ready models and systems.
• You bring demonstrated expertise in ML evaluation, benchmarking, and data quality.
• You are proficient in an ML framework of your choice, such as TensorFlow or PyTorch.
• You thrive in a fast-moving, ambiguous environment, turning open-ended customer and ML problems into clear priorities and production-ready solutions.
• You balance speed with quality, making pragmatic tradeoffs to deliver measurable progress without losing sight of production readiness.

• This role is hybrid (4 days a week in our Mountain View office)