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Clera

Founding Engineer

San Francisco$150k–$250kfulltimemidAdded 1 month ago

About this role

Join a well-funded Series A/B stealth startup as a Founding Engineer to build an AI-powered map and social discovery platform. You'll own end-to-end technical problems spanning search, recommendations, real-time infrastructure, and AI agents across hundreds of millions of places and events.

What you'll do

  • Build search and ranking systems for 200M+ places and millions of events at scale
  • Design recommendation and personalization features using embeddings and social signals
  • Develop real-time infrastructure for chat, presence, and location systems
  • Create AI agent pipelines for planning and reasoning over diverse data
  • Optimize Elasticsearch and vector search to sub-100ms latency
  • Build data pipelines to structure large-scale unstructured web data

What they're looking for

  • Full-stack development (TypeScript, React, React Native, iOS/Android) or Data/AI engineering (Python, Elasticsearch, Snowflake)
  • Search and ranking algorithms
  • Real-time and low-latency systems
  • Data processing pipelines and ML workflows
  • Infrastructure and performance optimization
  • LLMs and embedding systems
  • Early-stage startup experience
  • Cross-functional collaboration

Benefits

  • Salary: $150,000–$250,000 USD annually
  • Equity as a founding team member
  • High ownership and direct work with founders
  • Architectural decision-making authority
  • Fast-paced, high-impact environment
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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 time you optimized a production system for latency or scale. What was the bottleneck, and how did you approach it?
  • Tell us about your experience with search and ranking systems. Have you worked with Elasticsearch, vector databases, or similar technologies?
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