Machine Learning Engineer, Search Quality

Glean•Mountain View, CA

3d•$140,000 - $265,000•Hybrid

About The Position

Glean is seeking engineers to build search and assistant products for work. Engineers work across systems including query and document understanding, domain-adapted language models, natural-language question answering, evaluation, and experimentation. The role involves working with customers to understand their needs and using appropriate approaches to solve their problems. This position is hybrid, with four days per week in Glean’s Mountain View or San Francisco offices. The standard base salary range is $140,000–$265,000 annually. Compensation may depend on location, level, job-related knowledge, skills, and experience; certain roles may also be eligible for variable compensation and equity. Glean offers medical, vision, and dental coverage, a generous time-off policy, and the opportunity to contribute to a 401(k). New hires receive a home office improvement stipend, as well as annual education and wellness stipends. The company also holds regular events and provides healthy lunches daily.

Requirements

At least 2 years of experience.
BA/BS in computer science, mathematics, sciences, or a related field.
Experience working with search, recommendation, natural language processing, or other large systems involving machine learning.
Strong analytical skills and ability to work with data.
Demonstrated ability to design, build, and ship production-ready models.
Proficiency in an ML framework of choice.
Strong coding skills in languages such as Python, Go, Java, or C++.
Ability to thrive in a customer-focused, close-knit, cross-functional environment; willingness to take on work with the greatest impact for the company.
Proactive, positive approach to leading, learning, troubleshooting, and taking ownership of both small tasks and large features.

Responsibilities

Invent new signals to improve search-engine personalization.
Train models to capture interactions between signals in the ranking system.
Design improved methods for domain-adapting language models to each customer’s corpus.
Explore ways to combine large language models with search engines to answer complex questions.
Write robust code that is easy to read, maintain, and test.
Mentor more junior engineers or learn from experienced engineers.
Work with customers to understand their pain points and use appropriate tools to address them.