Why This Job is Featured on The SaaS Jobs
Building ML infrastructure for an enterprise search and assistant product sits at the center of several current SaaS shifts: retrieval-augmented generation, evaluation discipline, and the operational reality of serving AI features to business users. This role is featured because it focuses on the “platform layer” that determines whether ML capabilities become reliable product functionality, especially in a customer-facing context where relevance, latency, and trust matter.
From a SaaS career standpoint, the work maps closely to problems that recur across modern B2B software: designing data pipelines that can evolve with product scope, creating shared tooling that unblocks modelers, and establishing standards for testing and maintainability in ML-heavy systems. Experience here tends to translate well to other SaaS teams building search, recommendations, analytics, or GenAI features, because the underlying constraints—observability, iteration speed, and production robustness—are widely shared.
This role is best suited to engineers who prefer owning foundational systems over single-model work, and who like collaborating across functions (including direct customer interaction) to translate pain points into technical priorities. It will also fit someone comfortable balancing pragmatic delivery with long-horizon infrastructure decisions in a hybrid, office-linked environment.
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:
- 5+ years of experience
- BA/BS in computer science, math, sciences, or a related degree
- Experience working with ML infrastructure and engineering for search, recommendation, natural language processing, or something similar
- Proven ability to design, build, and ship production-ready software, ideally around ML infrastructure (pipelines, serving, GenAI)
- Strong Experience working with Apache Spark and hands-on experience with building, scaling, and operating large batch data pipelines
- 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 $200,000 - $280,000 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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