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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, Data Foundations - **Company:** Glean LLC - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Experienced - **Salary:** $140,000.0 - $265,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Microsoft Windows, Application Programming Interfaces (APIs), Artificial Intelligence, JIRA, C++ (Programming Language), Software as a Service, Data Infrastructure, Query Languages, Software Design Documents, Distributed Systems, Enterprise Content Management, Github, Information Retrieval, Python (Programming Language), Named Entity Recognition, NoSQL, Salesforce.Com, SQL Databases, Data Streaming, Data Ingestion, Large Language Models, Indexer, Backend, Slack, Gsuite, Virtual Agents, Webhooks, Data Pipelines, Servicenow, Golang - **Published:** July 16, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/p4rh39xou7 ## About the Role * 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. ## Description We are looking for a Software Engineer to join Glean's Data Foundations team - the group that owns the end-to-end data ingestion and management layer powering Glean's Search, AI Assistant, and Agent products across thousands of enterprise apps and billions of documents. Your work will directly determine the quality, freshness, and trustworthiness of the knowledge that every Glean user interacts with every day. You Will Work On * 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. ## Related Videos - [Improving quality with Agentic AI with Rovo Dev and Xray](https://www.wearedevelopers.com/videos/2005-improving-quality-with-agentic-ai-with-rovo-dev-and-xray) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Stack Overflow: Community and AI](https://www.wearedevelopers.com/videos/600-stack-overflow-community-and-ai) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Collaboration Quantified: Lessons from Open Source Developer Networks](https://www.wearedevelopers.com/videos/1422-collaboration-quantified-lessons-from-open-source-developer-networks) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) ## Related Articles - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [The Best X (Twitter) Accounts for Developers](https://www.wearedevelopers.com/magazine/294-the-best-x-twitter-accounts-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this)