Clera
Founding AI Engineer
About this role
A founding engineering role at an early-stage AI-powered social startup focused on building intelligent group matching and multi-channel AI agent systems that help young people form real-world friendships. You'll own core technical systems spanning recommendation algorithms, social graphs, and messaging orchestration while working directly with the founding team in San Francisco.
What you'll do
- Design and implement social graph and matching algorithms using collaborative filtering, embeddings, and reinforcement learning
- Build large-scale personalization systems that improve continuously based on real-world friendship outcomes
- Architect multi-channel AI agent workflows across SMS, Instagram, WhatsApp, and other messaging platforms
- Develop end-to-end user journey orchestration including onboarding, profiling, matching, and follow-ups
- Build scalable backend infrastructure connecting messaging APIs, third-party services, and core data layers
- Contribute to human-AI interaction design for natural, intuitive agent experiences
What they're looking for
- Production AI/ML systems development
- Recommendation systems and social graph algorithms
- Collaborative filtering and embedding techniques
- Reinforcement learning from user interactions
- Multi-channel messaging API integration
- AI agent frameworks and orchestration
- Full-stack backend infrastructure
- Data pipelines and personalization at scale
Benefits
- Founding-level equity package
- Competitive salary range
- On-site collaboration with founding team
- End-to-end technical ownership and impact
- Work on AI-driven social technology
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Clera
Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.
View all jobs at CleraLikely interview questions
- Walk us through a production recommendation or personalization system you've built—what were the key technical challenges and how did you measure success?
- How would you approach designing a matching algorithm that needs to continuously improve from real-world user interactions without explicit feedback?