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Listed location: Mountain View, CA
Work arrangement: onsite. A remote label does not confirm worldwide eligibility or visa sponsorship.
Read the employer’s description for qualifications, compensation and work eligibility. Confirm the position is still open on the application page.
Job description supplied by Glean; category and skill labels may be inferred. How our listings work · Report a problem
Job Description
- Build and scale connectors to a wide variety of SaaS and on-prem systems (Google Workspace, Microsoft 365, Slack, Salesforce, Jira, ServiceNow, GitHub, etc.).
- Handle full syncs, low-latency incremental updates via webhooks/APIs, rate-limiting, and complex authentication flows.
- Build advanced capabilities in datasources like actions, live-fetch, and query language support.
- Transform raw, unstructured enterprise content into rich, structured, permission-aware representations optimized for search and LLM reasoning.
- Design document schemas and enrichment pipelines (entity extraction, access-graph propagation, redactions, etc.).
- Expand the capabilities of AI products through deep integrations that allow us to automate tasks, perform complex queries grounded in enterprise data, and enhance our indexed corpus with live data.
- Own end-to-end correctness, freshness, and performance for petabyte-scale data flows.
- Solve hard problems in ordering, idempotency, exactly-once processing, backpressure, and retries across distributed queues, workers, and storage.
- Preserve fine-grained ACLs, deletions, and sensitivity constraints so AI answers are always grounded in what users are actually allowed to see.
- Partner closely with Search Serving, Product, Platforms, and Security teams to define how enterprise context is exposed to LLMs and agents.
- Continuously improve observability, alerting, and automation to onboard larger customers and more data sources with confidence.
- 3+ years building production backend or data infrastructure systems (Java, Go, C++, Python, etc.).
- Hands-on experience with distributed systems, data pipelines, queues, and large-scale storage (SQL/NoSQL).
- You think in SLOs, error budgets, failure modes, and correctness guarantees — not just features.
- Comfortable with strict consistency and permission-modeling challenges.
- Prior work on enterprise connectors, search/indexing, information retrieval, or security-sensitive systems is a strong plus.
- Passionate about making AI trustworthy by building the rock-solid data foundation underneath it.
- Power user of LLMs and AI tools in your own workflow.
- This role is hybrid (4 days a week in our Mountain View office)
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Skills mentioned
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Frequently asked questions
Is the Software Engineer, Data Foundations position at Glean remote?
The Software Engineer, Data Foundations role at Glean does not have a confirmed remote arrangement in our data. Check the employer description for its work location.
What type of employment is the Software Engineer, Data Foundations role?
Glean is hiring for a full-time Software Engineer, Data Foundations position.
Which skills are mentioned for the Software Engineer, Data Foundations job at Glean?
Detected skill labels include Python, Go, LLM, SQL, C++, Java, Distributed Systems. Check the employer description to distinguish required skills from preferred experience.
How do I apply for the Software Engineer, Data Foundations position at Glean?
You can apply for the Software Engineer, Data Foundations role directly through Glean's official application link provided on this page.
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