Why This Job is Featured on The SaaS Jobs
Machine Learning Engineer, Infrastructure sits at a core SaaS inflection point: turning AI features into dependable product capabilities. In a company building workplace search and an assistant, the infrastructure layer determines whether retrieval, evaluation, and experimentation can operate reliably across real customer data and varied enterprise environments. That makes the role relevant to the current wave of SaaS teams moving from prototypes to production-grade AI systems.
From a SaaS career perspective, this position builds durable leverage: designing pipelines and serving foundations that other ML engineers depend on. The work naturally develops judgment around operational readiness, reproducibility, and measurement, which are central to shipping AI in subscription products where reliability and iteration cycles matter. Regular customer interaction also reinforces the habit of grounding technical decisions in usage patterns and product constraints rather than purely model metrics.
This role tends to suit engineers who prefer platform ownership, clear interfaces, and enabling others over focusing exclusively on modeling. It aligns well with someone who enjoys cross-functional collaboration and steady problem decomposition, and who wants to deepen end-to-end understanding of how ML systems behave in production SaaS settings, from data flow through evaluation and ongoing maintenance.
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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