Cleric builds a self-learning AI site reliability engineer (SRE) agent that autonomously investigates incidents, learns from feedback, and accelerates issue resolution for software teams.
JOIN US AT CLERIC We’re building a future where engineers are focused on designing and building products, freeing them from operational toil. We’re starting with an AI-powered SRE agent that remediates issues in production environments autonomously. It uses an LLM-based reasoning engine to react to, interpret, and implement solutions to production issues, even those it's encountering for the first time. Now is a divine time to join. We're a small group of veterans in AI, software, and infrastructure, backed by a leading AI venture capital firm and Silicon Valley angels. Our product is in production at high-scale technology companies in fintech, ride-hailing, and autonomous vehicles. ABOUT THE ROLE We’re hiring a Staff Software Engineer, AI. You will drive the development of our agent. You will be working on the reasoning, learning, and integrations of our SRE agent, and also the underlying runtime and technical architecture of the complete product. You'll help keep engineering standards high, define the experience for our customers, and push the agent to solve increasingly more complex SRE challenges in a fundamentally non-deterministic environment where correctness is measured through evaluation, not code inspection. WHAT YOU'LL DO: - Design and implement agent evaluation frameworks to measure performance and reliability - Debug agent behavior, not just code: trace the agent’s reasoning, tool choices, and context to understand why it made specific decisions. - Build autonomous agents capable of executing complex, multi-step tasks - Develop self-improving systems that can adapt based on feedback and results - Expand agent observability to resolve day-to-day issues with tool-calling and agent orchestration YOU HAVE: - You’ve spent years building and operating production systems and have a foundation in ML fundamentals. You understand recall, precision, and F1 scores, and why accuracy alone is misleading. - You’ve built at least one agent end-to-end. - You regularly use coding agents and AI assistants for real work and have opinions about where they fail or succeed. - You have strong software engineering fundamentals and have opinions on how to build robust, scalable, and secure software. - You’ve operated high-scale distributed infrastructure. You’ve seen it done right, and you’ve seen it gone wrong. Kubernetes, Kafka, Spark, microservices, and all the usual suspects. - You've built complex systems at scale. We use Python, but value experience with systems languages (Rust, C++, Go) or JVM languages (Kotlin, Scala). - You are a methodical thinker and try to understand the big picture - You challenge assumptions and propose pragmatic solutions NICE TO HAVE: - Deep SRE or platform engineering experience - Previous startup experience HOW WE WORK - Small teams, big impact: We believe that small teams can deliver great products. - Culture matters: We value radical candor in a positive and inclusive work environment. - In-person collaboration: We believe in working closely to deliver the best results. - AI-first approach: We don't simply build AI products; we augment ourselves with it. INTERVIEW PROCESS (YOU'LL MEET MOST OF THE TEAM VIA THE PROCESS) 1. Intro Call - Discuss your experience, the company, product, and the role - Lightning tech screen (10 mins) on agent building fundamentals and engineering practices 2. Software Engineering Session (1 hour) - Collaboratively build an application - Focus on practical software engineering, not algorithm challenges 3. System Design Session (90 mins) - Work through a system design problem relevant to your daily work 4. Bar Raiser (60 mins) - Product thinking - Engineering practices