Machine Learning Engineer, Enterprise Brain
GleanMountain View, CAposted 6h ago
- PayUSD / yr
- $200k – 280k
- Where
- Mountain View, CA
- Posted
- Oct 16
- 6 hours ago
- Type
- Full-time
- Setup
- Hybrid
At a glance
Build the Enterprise Brain’s proactive AI systems using LLMs, reinforcement learning, fine-tuning, ranking, and evaluation pipelines to predict user needs and automate enterprise workflows at scale.
Summarized by AI from the original posting
What you'll do
- Work on ML problems involving user understanding and task prediction
- Invent LLM workflows and signals for reasoning, planning, and personalization
- Design and optimize reinforcement learning and fine-tuning approaches
- Lead scalable evaluation, benchmarking, and optimization loops
- Build and maintain ML pipelines for enterprise and knowledge graph construction
- Measure, monitor, and improve data quality, model quality, and end-to-end system performance
- Collaborate cross-functionally to deliver production-ready ML solutions
- Mentor junior engineers
Requirements
- 01Proven ability to design, build, and ship production-ready models and systems
- 02Demonstrated expertise in ML evaluation, benchmarking, and data quality
- 03Proficiency in an ML framework such as TensorFlow or PyTorch
- 04Ability to solve open-ended customer and ML problems
- 05Ability to deliver production-ready solutions in ambiguous, fast-moving environments
Perks
Skills
Full description
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)
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