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Clera

Founding AI Engineer

San Francisco$150k–$200kfulltimemidAdded today

About this role

Join a founding team in San Francisco building an AI-powered platform that matches young people into compatible social groups and delivers experiences through multi-channel AI agents. You'll own two core technical pillars: AI matching systems and social graphs, plus multi-channel agent orchestration across messaging platforms.

What you'll do

  • Design and build social graphs from real-world interaction data to power AI-driven group matching
  • Develop collaborative filtering, graph embeddings, and ranking systems for personalized compatibility matching
  • Apply reinforcement learning from user outcomes to continuously improve matching and personalization
  • Architect multi-channel AI agent workflows operating natively across Instagram, iMessage, WhatsApp, TikTok, and other platforms
  • Orchestrate multiple AI agents, APIs, and data layers into seamless user experiences at scale
  • Build scalable, production-grade infrastructure for AI workloads and agent coordination

What they're looking for

  • AI/ML systems design and production deployment
  • Collaborative filtering and recommendation systems
  • Graph embeddings and graph-based representations
  • Reinforcement learning from real-world feedback
  • Multi-agent orchestration and API integration
  • Distributed systems and data pipelines
  • Cloud infrastructure (AWS, GCP, or Azure)
  • Full-stack software engineering and cross-functional collaboration

Benefits

  • Meaningful founding-level equity with significant upside potential
  • Base salary $150,000–$200,000 per year
  • Ground-floor opportunity at early-stage company
  • Fully on-site team environment in San Francisco
  • Work on a mission addressing Gen Z loneliness
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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.

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Likely interview questions

  • Tell us about a production AI/ML system you built for matching or personalization—what was the hardest technical challenge?
  • Walk us through how you've deployed reinforcement learning models using real-world feedback signals.
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