採用
福利厚生
•Healthcare
•401(k)
•Equity
•Home Office
•Learning
•Meals
必須スキル
Backend development
Distributed systems
Data pipelines
Java
Go
C++
Python
About Glean: Founded in 2019, Glean is an innovative AI-powered knowledge management platform designed to help organizations quickly find, organize, and share information across their teams. By integrating seamlessly with tools like Google Drive, Slack, and Microsoft Teams, Glean ensures employees can access the right knowledge at the right time, boosting productivity and collaboration. The company’s cutting-edge AI technology simplifies knowledge discovery, making it faster and more efficient for teams to leverage their collective intelligence.
Glean was born from Founder & CEO Arvind Jain’s deep understanding of the challenges employees face in finding and understanding information at work. Seeing firsthand how fragmented knowledge and sprawling SaaS tools made it difficult to stay productive, he set out to build a better way - an AI-powered enterprise search platform that helps people quickly and intuitively access the information they need. Since then, Glean has evolved into the leading Work AI platform, combining enterprise-grade search, an AI assistant, and powerful application- and agent-building capabilities to fundamentally redefine how employees work.
About the Role:
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:
Ingestion & Connectivity:
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Build and scale connectors to a wide variety of SaaS and on-prem systems (Google Workspace, Microsoft 365, Slack, Salesforce, Jira, ServiceNow, GitHub, etc.).
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Handle full syncs, low-latency incremental updates via webhooks/APIs, rate-limiting, and complex authentication flows.
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Build advanced capabilities in datasources like actions, live-fetch, and query language support.
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Data Processing & Modeling
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Transform raw, unstructured enterprise content into rich, structured, permission-aware representations optimized for search and LLM reasoning.
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Design document schemas and enrichment pipelines (entity extraction, access-graph propagation, redactions, etc.).
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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.
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Reliability & Distributed Systems
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Own end-to-end correctness, freshness, and performance for petabyte-scale data flows.
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Solve hard problems in ordering, idempotency, exactly-once processing, backpressure, and retries across distributed queues, workers, and storage.
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Security & Permissions
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Preserve fine-grained ACLs, deletions, and sensitivity constraints so AI answers are always grounded in what users are actually allowed to see.
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Cross-Functional Impact
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Partner closely with Search Serving, Product, Platforms, and Security teams to define how enterprise context is exposed to LLMs and agents.
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Continuously improve observability, alerting, and automation to onboard larger customers and more data sources with confidence.
About you:
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3+ years building production backend or data infrastructure systems (Java, Go, C++, Python, etc.).
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Hands-on experience with distributed systems, data pipelines, queues, and large-scale storage (SQL/NoSQL).
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You think in SLOs, error budgets, failure modes, and correctness guarantees — not just features.
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Comfortable with strict consistency and permission-modeling challenges.
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Prior work on enterprise connectors, search/indexing, information retrieval, or security-sensitive systems is a strong plus.
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Passionate about making AI trustworthy by building the rock-solid data foundation underneath it.
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Power user of LLMs and AI tools in your own workflow.
Location:
- This role is hybrid (4 days a week in one of our SF Bay Area offices)
Compensation & Benefits: The standard base salary range for this position is $140,000 - $265,000 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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模擬応募者数
0
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0
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Gleanについて

Glean
Series BGlean Technologies, Inc. is an American technology company specializing in enterprise-grade artificial intelligence (AI) and search capabilities.
1-50
従業員数
New York
本社所在地
$2.2B
企業価値
レビュー
3.5
1件のレビュー
ワークライフバランス
4.0
報酬
3.0
企業文化
4.0
キャリア
3.8
経営陣
3.5
65%
友人に勧める
良い点
Flexibility and choice in team placement
AI-focused work opportunities
Better work culture
改善点
Lower compensation (~$8k difference)
Potential data sharing regulation issues
Regulatory compliance concerns
給与レンジ
47件のデータ
Junior/L3
Junior/L3 · Solution Architect
0件のレポート
$62,409
年収総額
基本給
-
ストック
-
ボーナス
-
$53,048
$71,770
面接体験
2件の面接
難易度
3.5
/ 5
期間
14-28週間
体験
ポジティブ 0%
普通 50%
ネガティブ 50%
面接プロセス
1
Application Review
2
Online Assessment
3
Technical Phone Screen
4
Final Interview
5
Team Matching
6
Offer
よくある質問
Coding/Algorithm
Technical Knowledge
System Design
Behavioral/STAR
ニュース&話題
Bright Uro Expands Early Clinical Adoption of Glean Urodynamics System Across Key U.S. Networks - TipRanks
TipRanks
News
·
3d ago
Glean interview
Does anyone have experience at Glean and/or their interview process? This sub seems to have neutral to negative feelings towards glean but nothing that recent. Would love to hear it all - good, bad, ugly, or run? TIA
·
4d ago
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1
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4
Glean Gains Fourth Consecutive Inclusion on Forbes AI 50 List - TipRanks
TipRanks
News
·
4d ago
Glean Receives Fourth Consecutive Recognition on Forbes AI 50 List - TipRanks
TipRanks
News
·
5d ago