採用
福利厚生
•Healthcare
•401(k)
•Equity
•Home Office
•Learning
•Mental Health
•Meals
必須スキル
Python
Go
Java
C++
Distributed Systems
Kubernetes
Cloud Platforms
Observability
Debugging
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:
The Agents Runtime team builds the low‑latency, reliable, and secure foundation that powers Glean’s AI agents and assistant experiences at scale. You’ll design and operate core runtime services for multi‑turn orchestration, tool calling, model routing, memory, streaming, and safety. You’ll work across distributed systems, production observability, and ML infra integrations to deliver an experience that feels instant, accurate, and trustworthy — while optimizing cost and reliability.
You will:
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Own impactful runtime problems end‑to‑end — from architecture and design to production launch and ongoing reliability.
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Build and evolve core services for session lifecycle, streaming responses (e.g., gRPC/WebSockets), structured tool execution, memory/state, and policy/guardrails.
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Design for performance, correctness, and cost: reduce p50/p95 latency, improve tail behavior, and optimize token/tool budgets.
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Integrate with leading LLM providers (e.g., OpenAI, Anthropic, Google Gemini) and internal evaluation frameworks to improve quality and predictability.
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Harden the platform with fault isolation, retries, timeouts, circuit‑breaking, backpressure, and graceful degradation.
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Instrument deep observability (tracing, metrics, logs) and create playbooks/SLOs for high availability and on‑call excellence.
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Collaborate closely with product, quality, and application teams to prioritize the most impactful roadmap investments.
You are:
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3+ years of software engineering experience building production distributed systems or cloud‑native applications.
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BS/BA in Computer Science or related field, or equivalent practical experience.
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Strong coding skills in at least one of: Python, Go, Java, or C++, with a focus on reliability, performance, and tests.
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Product‑minded: you prioritize customer impact, clear SLAs/SLOs, and pragmatic iteration.
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Ownership‑driven with a positive, proactive attitude; comfortable leading projects or learning from battle‑tested engineers.
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Experience operating services on Kubernetes and at least one major cloud (e.g., GCP, AWS, or Azure).
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Familiarity with event/streaming systems (e.g., Pub/Sub, Kafka), caching (e.g., Redis), and data stores for low‑latency paths.
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Practical understanding of LLM/agents building blocks: tool/function calling, structured outputs, streaming, and model selection/routing.
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Strong observability and debugging skills: tracing (e.g., Open Telemetry), metrics, dashboards, and production forensics.
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Background in one or more areas is a plus: policy/guardrails, multi‑tenant isolation, rate‑limiting, concurrency control, cost optimization.
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 $170,000 - $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.
#LI_HYBRID
総閲覧数
2
応募クリック数
0
模擬応募者数
0
スクラップ
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
·
4d 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
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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
·
5d ago
Glean Receives Fourth Consecutive Recognition on Forbes AI 50 List - TipRanks
TipRanks
News
·
5d ago