Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI
API @ 3
Experimentation @ 3
Go @ 6
Java @ 6
LLM @ 3
Machine Learning @ 3
Mathematics @ 3
NLP
Python @ 6
Search Engines
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Glean is building a Work AI platform that combines enterprise search, an AI Assistant, and scalable AI agents. The platform uses enterprise and personal knowledge graphs, more than 100 SaaS connectors, flexible LLM choices, and robust APIs.
The engineering team works across query understanding, document understanding, domain-adapted language models, natural language question answering, evaluation, and experimentation to build search and assistant products for the workplace.
Responsibilities
- Invent new signals to improve search 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.
- Find new ways to combine large language models with search engines to answer complex questions.
- Write robust, readable, maintainable, and testable code.
- Mentor junior engineers and learn from experienced engineers.
- Work closely with customers and cross-functional teams to understand pain points and deliver impactful solutions.
Requirements
- 2+ years of experience.
- Bachelor's degree in computer science, mathematics, science, or a related field.
- Experience with search, recommendation systems, natural language processing, or other large machine-learning systems.
- Strong analytical and data skills.
- Demonstrated ability to design, build, and ship production-ready models.
- Proficiency with an ML framework of choice.
- Strong coding skills in Python, Go, Java, C++, or similar languages.
- Ability to work in a customer-focused, close-knit, and cross-functional environment.
- Team-oriented attitude and willingness to take on the work that is most impactful for the company.
- Proactive and positive approach to leading, learning, troubleshooting, and taking ownership of tasks and features.
Work Location
This is a hybrid role requiring four days per week in Glean's San Francisco office.
Compensation And Benefits
The standard base salary range is $140,000–$265,000 annually. Compensation depends on factors including location, level, job-related knowledge, skills, and experience. Certain roles may also be eligible for variable compensation, equity, and benefits.
Benefits include medical, vision, and dental coverage, generous time off, a 401(k) plan, a home-office improvement stipend, annual education and wellness stipends, company events, and daily healthy lunches.
Glean is committed to building and sustaining a diverse and inclusive workplace. The interview process includes a brief AI-focused exercise or discussion covering how candidates think about, design, and use AI.