Clera
Software Engineer
About this role
Full-stack software engineer needed to design and build an AI agent platform serving the hospitality industry. You'll own the end-to-end system handling guest communication and operations at scale, from response pipelines and knowledge retrieval to tool orchestration and observability.
What you'll do
- Design response generation pipelines including routers, orchestrators, and synthesizers for intent classification and tool selection
- Develop knowledge retrieval systems that ground agent responses in accurate customer data
- Build tool orchestration layers that execute multi-step workflows with graceful failure handling
- Create evaluation frameworks measuring quality against real conversations to catch regressions
- Implement observability and tracing that makes agent reasoning transparent to support teams
- Extend platform to voice conversations with real-time transcription and tool-use capabilities
What they're looking for
- Production AI/ML systems design and debugging
- Retrieval systems and semantic search
- Multi-step workflow and function-calling orchestration
- Backend development (Python, Go, Rust, or Java)
- Observability, tracing, and debugging tooling
- Third-party API integration and failure handling
- Model routing and prompt optimization
- Full-stack development across infrastructure and data pipelines
Benefits
- Equity stake (0.20% to 1.20%)
- Comprehensive health insurance
- 401(k) match
- Relocation support available
- Meaningful product ownership and autonomy over architecture decisions
- Small, flat team 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.
View all jobs at CleraLikely interview questions
- Tell us about a time you shipped a production AI/ML system—what went wrong and how did you debug it?
- How have you approached building and evaluating retrieval systems in the past, and how did you measure their effectiveness?