Join us at ClericWe'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 diagnosis and remediates issues in production environments. 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.
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 RoleHelp build the systems that power our autonomous agent. You'll work directly with our founding team and enterprise customers, shipping features that impact production environments at scale.
Required:Strong CS fundamentals and software engineering experience
Experience building software and augmenting yourself with LLMs (GPT, Claude, etc.)
Proficiency in Python and at least one of the following::
Infrastructure (Cloud platforms, Kubernetes)
Frontend development (TypeScript, React)
Proven builder mindset (open source, hackathons, side projects)
What Makes This Internship Special: Build AI products used by major tech companies
Work across our entire stack, from agent logic to customer interfaces
Learn from experienced engineers who've built at scale
Ship features directly to enterprise customers
What You'll Build: Agent capabilities and evaluation frameworks
Customer facing features and debugging tools
Infrastructure for testing and deployment
Integration with enterprise systems
Internal tooling to augment our team with AI
How we workSmall 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:1. Brief intro chat (30 mins)
2. Take-home project: Build a focused AI agent
3. Technical deep-dive: Architecture discussion and code review (60 mins)
4. Team lunch/dinner
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