About the Role:
Glean’s Enterprise Brain team builds applied ML systems that help Glean understand a user’s work and anticipate useful next steps, including task prediction and proactive assistance. The work combines LLMs and traditional ML with signals from the Enterprise Graph, ranking and retrieval, and orchestration. You’ll own production ML problems from problem definition and evaluation through launch and monitoring, with a focus on measurable improvements to product quality.
You will:
- Define and solve applied ML problems involving user-work understanding, task prediction, and proactive assistance.
- Combine LLMs and traditional ML with Enterprise Graph signals, ranking/retrieval, and orchestration to improve product quality.
- Build and ship production ML systems, including the evaluation, benchmarking, and data-quality loops needed to measure their impact.
- Own the lifecycle from problem definition and baseline through launch, monitoring, and iteration.
- Partner with product and engineering to understand customer problems and deliver reliable, production-ready solutions
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)
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Skills Required
- 6+ years of industry experience in machine learning engineering
- BA or BS in computer science, mathematics, sciences, or a related field
- Ability to design, build, and ship production-ready models and systems
- Expertise in machine learning evaluation, benchmarking, and data quality
- Proficiency in an ML framework such as TensorFlow or PyTorch
- Ability to work in a fast-moving, ambiguous environment and turn open-ended problems into production-ready solutions
- Ability to balance delivery speed, quality, and production readiness
Glean Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Glean and has not been reviewed or approved by Glean.
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Healthcare Strength — Healthcare coverage includes employer-provided medical, dental, and vision across U.S. roles. Descriptions of benefits and third‑party listings characterize core health offerings as solid.
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Equity Value & Accessibility — Equity is widely offered via stock options and highlighted as a core part of total rewards. Engineering packages are portrayed as particularly competitive when equity value is considered.
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Leave & Time Off Breadth — Time off includes flexible PTO and a company‑wide winter break. Parental leave is described as generous in public listings, though specifics are not consistently published.
Glean Insights
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