Why This Job is Featured on The SaaS Jobs
Machine learning roles inside SaaS products sit at the intersection of user intent, content understanding, and measurable product outcomes. This position is notable because it is anchored in enterprise search and assistant functionality—an area where SaaS companies differentiate through relevance, trust, and continuous improvement driven by real workplace data and feedback loops.
For a long-term SaaS career, the work described maps to durable responsibilities: building production-grade models, defining signals that influence ranking and personalization, and operating within experimentation and evaluation cycles. That combination tends to develop strong instincts for model performance in real environments—latency, robustness, monitoring, and iteration—rather than research in isolation. Regular customer interaction also points to an applied ML track where product constraints and user workflows shape technical decisions.
This role best suits engineers who like moving across the stack of an ML-powered SaaS feature, from data and modeling through to maintainable code and measurable impact. It also fits professionals who prefer cross-functional collaboration and practical problem selection, and who are comfortable balancing near-term improvements with longer-horizon model and system design.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the Role:
Glean is looking for engineers to help build the world’s best search and assistant product for work. Our engineers work on a range of systems across the stack, including query understanding, document understanding, domain-adapted language models, natural language question-answering, evaluation, and experimentation. We interact regularly with customers, deeply understand their pain points, and use whatever tool is necessary, simple or complex, to solve their problems.
You will:
- Invent new signals to improve the personalization of our search engine
- Train a model to capture interactions between signals in our ranking system
- Design smarter ways to domain-adapt language models to each customer’s corpus
- Discover new ways of combining LLMs with search engines to answer complex questions
- Write robust code that’s easy to read, maintain, and test
- Mentor more junior engineers, or learn from battle-tested ones
About you:
- 2+ years of experience
- BA/BS in computer science, math, sciences, or a related degree
- Experience working with search, recommendation, natural language processing, or other large systems involving machine learning
- Strong analytical skills and ability to work with data
- Proven ability to design, build, and ship production-ready models
- Proficiency in your ML framework of choice
- Strong coding skills (Python, Go, Java, C++, ...)
- Thrive in a customer-focused, tight-knit and cross-functional environment - being a team player and willing to take on whatever is most impactful for the company is a must
- A proactive and positive attitude to lead, learn, troubleshoot and take ownership of both small tasks and large features
Location:
- This role is hybrid (3-4 days a week in one of our SF Bay Area offices)
Compensation & Benefits:
The standard base salary range for this position is $140,000 - $265,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.
We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan to support your long-term goals. When you join, you'll receive a home office improvement stipend, as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events, and provide healthy lunches daily to keep you fueled and focused.
We are a diverse bunch of people and we want to continue to attract and retain a diverse range of people into our organization. We're committed to an inclusive and diverse company. We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race.
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