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Clera

Forward Deployed Engineer

San Francisco$150k–$200kfulltimemidAdded today

About this role

Join an early-stage AI/ML startup as a Forward Deployed Engineer where you'll manage the complete lifecycle of customer deployments, from integration through production. You'll work directly with the executive team, embed in customer environments, and drive product improvements based on real-world usage patterns and failure modes.

What you'll do

  • Own end-to-end customer deployments including integration, troubleshooting, and code contributions to customer repositories
  • Embed in customer Slack channels as the technical expert on their AI agent architecture and failure patterns
  • Develop custom detection logic and evaluation frameworks for each customer's specific use cases
  • Open and defend pull requests in unfamiliar codebases, representing the technical team in code review discussions
  • Lead critical conversations around security reviews, deployment architecture, and customer trust-building
  • Identify generalizable patterns from customer insights and feed learnings back to inform product development

What they're looking for

  • Production software development (3+ years)
  • TypeScript and code patching in unfamiliar systems
  • AI agent development and production deployment
  • Detection logic, evals, and observability/monitoring
  • Rapid system comprehension under pressure
  • Customer-facing technical engagement
  • Security reviews and deployment architecture
  • Incident investigation and debugging

Benefits

  • Direct reporting to CEO and CTO with high visibility
  • Visa sponsorship available
  • Customer-facing role with direct product impact
  • On-site in San Francisco with customer site travel
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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

  • Describe a time you deployed an AI agent or production system that failed unexpectedly—what went wrong and how did you diagnose it?
  • Tell us about a customer-facing technical engagement where you had to quickly understand an unfamiliar system and identify critical issues.
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