Machine Learning Engineer: Search Quality & Personalization

DataJobs; San Francisco (CA); Hybrid; USD 140,000 - 265,000; Full time

Benefits offered by this job

Medical, Vision, and Dental

Home office stipend

Education and wellness stipends

Healthy daily lunches

Job summary

Glean is seeking a Machine Learning Engineer focused on Search Quality to build and improve its search and assistant capabilities. You will work on query/document understanding, domain-adapted language models, and personalized ranking strategies.

The role emphasizes experimentation, cross-functional collaboration, and mentorship, with a hybrid work setup in San Francisco and competitive compensation.

Qualifications

• 2+ years of experience in ML or related field.
• BA/BS in computer science, math, sciences, or a related degree.
• Experience with search, recommendation, NLP, or large ML systems.
• Ability to design, build, and ship production-ready models.
• Proficiency in your machine learning framework of choice.
• Strong coding skills in Python, Go, Java, or C++.
• Ability to thrive in a customer-focused, cross-functional environment.
• Proactive and ownership-oriented approach to tasks and features.

Responsibilities

• Develop new signals aimed at improving personalization across Glean’s search engine.
• Train models that capture interactions among signals within the ranking system.
• Create domain-adapted language models to each customer’s corpus.
• Explore methods for combining LLMs with search engines to answer complex questions.
• Write robust, readable, testable code.
• Mentor more junior engineers, or learn from experienced team members.

Skills

2+ years experience

Analytical skills

Team player

Ownership mindset

ML framework proficiency

Education

BA/BS in CS or related

Tools

Python

Go

Java

C++

LLMs

Machine learning

Job description

Glean is seeking a Machine Learning Engineer focused on Search Quality to build and improve its search and assistant capabilities. You will work on query/document understanding, domain-adapted language models, and personalized ranking strategies.

The role emphasizes experimentation, cross-functional collaboration, and mentorship, with a hybrid work setup in San Francisco and competitive compensation.