Machine Learning Engineer, Enterprise Brain
At a glance
Mid-level Machine Learning Engineering role at Glean. Mountain View · $200,000–$280,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Develop ML methods that infer what users need and anticipate tasks they may want automated
- Create LLM workflows and signals that strengthen reasoning, planning and personalized results
- Tune models with reinforcement learning and fine-tuning, then build evaluation and benchmarking cycles
- Maintain ML pipelines for enterprise and knowledge graph data, improving data and model quality
- Work with other teams to turn customer problems into production-ready ML systems
What you bring
- At least six years working in machine learning engineering
- A bachelor's degree in computer science, math, sciences or a related area
- Experience building and launching production models and systems, with strength in evaluation, benchmarking and data quality
- Proficiency with an ML framework such as TensorFlow or PyTorch
Who this fits
This role suits an experienced ML engineer who can turn unclear customer and technical problems into concrete production work, balancing delivery speed with quality. You'll work from the Mountain View office four days each week. The role includes mentoring junior engineers or learning from experienced colleagues.
From the employer
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)
By clicking “Submit Application,” I confirm that I have read the Global Data Privacy Notice and the Applicant Arbitration Agreement, and I agree to the terms.