Used Tools & Technologies
Machine LearningRequired Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Go @ 7
Kafka @ 3
Kubernetes @ 4
Redis @ 3
Python @ 7
GCP @ 4
Java @ 7
Distributed Systems @ 4
AWS @ 4
Azure @ 4
gRPC @ 4
CCPA @ 4
GDPR @ 4
Debugging @ 7
API @ 4
Engineering Management @ 4
LLM @ 4
OpenTelemetry @ 7
Observability @ 4
AI @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
About Glean
Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business.
Role summary
The Tech Lead Manager of the Agentic Runtime team builds the low-latency, reliable, and secure foundation that powers Glean’s AI agents and assistant experiences at scale. You will design and operate core runtime services for multi-turn orchestration, tool calling, model routing, memory, streaming, and safety. Work spans distributed systems, production observability, and ML infra integrations to deliver an experience that feels instant, accurate, and trustworthy while optimizing cost and reliability.
Responsibilities
- Own impactful runtime problems end-to-end — from architecture and design to production launch and ongoing reliability.
- Build and evolve core services for session lifecycle, streaming responses (e.g., gRPC/WebSockets), structured tool execution, memory/state, and policy/guardrails.
- Design for performance, correctness, and cost: reduce p50/p95 latency, improve tail behavior, and optimize token/tool budgets.
- Integrate with leading LLM providers (e.g., OpenAI, Anthropic, Google Gemini) and internal evaluation frameworks to improve quality and predictability.
- Harden the platform with fault isolation, retries, timeouts, circuit-breaking, backpressure, and graceful degradation.
- Instrument deep observability (tracing, metrics, logs) and create playbooks/SLOs for high availability and on-call excellence.
- Collaborate closely with product, quality, and application teams to prioritize the most impactful roadmap investments.
Requirements
- 8+ years of software engineering experience building production distributed systems or cloud-native applications.
- 1+ years of engineering management experience.
- BS/BA in Computer Science or related field, or equivalent practical experience.
- Strong coding skills in at least one of: Python, Go, Java, or C++, with a focus on reliability, performance, and tests.
- Product-minded: prioritize customer impact, clear SLAs/SLOs, and pragmatic iteration.
- Ownership-driven with a positive, proactive attitude; comfortable leading projects or learning from battle-tested engineers.
- Experience operating services on Kubernetes and at least one major cloud (e.g., GCP, AWS, or Azure).
- Familiarity with event/streaming systems (e.g., Pub/Sub, Kafka), caching (e.g., Redis), and data stores for low-latency paths.
- Practical understanding of LLM/agents building blocks: tool/function calling, structured outputs, streaming, and model selection/routing.
- Strong observability and debugging skills: tracing (e.g., OpenTelemetry), metrics, dashboards, and production forensics.
- 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 (4 days a week in either our Mountain View or San Francisco offices).
Compensation & Benefits
- Standard base salary range: $250,000 - $300,000 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.
- Benefits include Medical, Vision, and Dental coverage, generous time-off policy, 401(k) plan contributions, home office improvement stipend, and annual education and wellness stipends. Regular company events and daily healthy lunches are provided.
AI-First Mindset at Glean
AI fluency is core to how Glean works. As part of the interview process, candidates complete a brief AI-focused exercise or discussion to demonstrate how they think about, design, and use AI to drive impact in their role.
Global Data Privacy Notice for Job Candidates and Applicants
Depending on your location, GDPR, CCPA, or other privacy laws may regulate how Glean manages applicant data. The full notice is available in Glean's Privacy Policy. US applicants and their applications are subject to arbitration as outlined in the Applicant Arbitration Agreement.