Why this role stands out
Advance your career as a Machine Learning Engineer at Glean, a leader in Work AI innovation, where you'll contribute to cutting-edge AI solutions with significant business impact. This hybrid role is ideal for skilled engineers eager to develop advanced ML models and thrive in a collaborative, fast-paced environment recognized for its groundbreaking work. Apply now to join a company redefining enterprise knowledge and productivity.
Quick overview
Job description
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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