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Clera

Founding AI Engineer

San Francisco$150k–$200kfulltimemidAdded today

About this role

Join a founding team building AI-powered social matching for Gen Z to combat loneliness. You'll architect machine learning systems for group matching, design multi-channel AI agent experiences across messaging platforms, and scale infrastructure supporting millions of users across university campuses.

What you'll do

  • Design and build social graph algorithms using collaborative filtering and graph embeddings to match compatible friend groups
  • Apply reinforcement learning from real-world interaction outcomes to continuously improve matching quality
  • Develop AI agent workflows that operate natively across Instagram, iMessage, WhatsApp, TikTok, and other messaging channels
  • Orchestrate multi-agent infrastructure handling onboarding, profiling, logistics, reminders, and follow-ups
  • Architect scalable backend systems processing millions of data points to support AI workloads and agent orchestration
  • Collaborate cross-functionally with product, design, and data science teams on user experience and infrastructure decisions

What they're looking for

  • Recommender systems and collaborative filtering
  • Graph neural networks and graph embeddings
  • Reinforcement learning from human feedback
  • Multi-agent orchestration and AI agent design
  • Messaging API integration (WhatsApp, Instagram, iMessage, TikTok)
  • Large-scale data processing and ML infrastructure
  • Ranking and personalization systems
  • Distributed systems and backend architecture

Benefits

  • Meaningful founding equity package
  • Competitive salary and benefits for early-stage well-resourced startup
  • On-site team environment in San Francisco
  • High ownership and end-to-end problem-solving opportunities
  • Work on high-impact consumer social product addressing Gen Z needs
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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

  • Walk us through a recommender or matching system you built in production—how did you measure success and iterate?
  • Describe your experience applying reinforcement learning to personalization. What feedback signals did you use and how did you handle exploration vs. exploitation?
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