Machine Learning Engineer, Infrastructure
Glean
Why This Job is Featured on The SaaS Jobs
Machine Learning Engineer, Infrastructure roles sit at the heart of modern SaaS product differentiation, especially for companies building AI-assisted workflows and enterprise search. This listing stands out because it points to systems that must operate reliably under real customer usage, where retrieval, evaluation, and experimentation become product capabilities rather than research projects. The hybrid Bay Area setup also signals close collaboration with product and customer-facing teams, a common pattern for SaaS firms iterating on AI features in production.
From a SaaS career perspective, infrastructure work on ML and data pipelines is durable experience that transfers across AI-first and traditional SaaS businesses adopting LLMs. Building serving and pipeline foundations, enabling model-focused engineers, and improving robustness creates exposure to the operational side of ML, including maintainability, observability, and iteration loops tied to user outcomes. Regular customer interaction adds a practical feedback channel that helps engineers develop product judgment alongside technical depth.
This role best fits engineers who prefer platform-building over single-model ownership, and who enjoy making other teams more effective through shared systems. It suits professionals comfortable balancing clean engineering with pragmatic problem-solving, and those who value cross-functional collaboration where technical decisions are informed by real usage patterns.
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 generative AI, RAG, 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:
Design, build, and improve ML systems and Data pipelines infrastructure
Work with and enable other ML engineers focused on modeling
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
Proven ability to design, build, and ship production-ready software, ideally around AI/ML infrastructure (Pipelines, Serving etc)
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 $175,000 - $270,000 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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