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Clera

Founding AI Engineer

remote (Remote)$130k–$165kfulltimemidAdded today

About this role

Build evaluation systems and feedback loops for an AI-powered B2B pricing platform. You'll own LLM infrastructure, model routing across frontier providers, and observability tools that help the AI earn trust through measurable customer revenue outcomes.

What you'll do

  • Design and build evaluation harnesses and benchmarks using tracked pricing outcomes as ground truth for AI recommendations
  • Own LLM infrastructure and model routing across Anthropic, Google, and other providers with explicit cost/latency/quality tradeoffs
  • Maintain EU data-residency boundaries for customer model calls to ensure compliance with data regulations
  • Extend MCP server infrastructure so LLM agents can programmatically drive the platform
  • Build observability and feedback loops that surface where AI earns or loses trust from human reviewers
  • Communicate non-deterministic engineering concepts clearly to non-technical stakeholders

What they're looking for

  • 8+ years software engineering with recent production LLM experience
  • LLM evaluation and observability system design
  • TypeScript and Python
  • FastAPI backend development
  • GCP infrastructure
  • Model routing and multi-provider LLM orchestration
  • MCP (Model Context Protocol) development
  • EU data compliance and residency requirements

Benefits

  • Meaningful employee stock option plan (ESOP)
  • Fully remote-friendly role based in Amsterdam with relocation support considerations
  • Work on applied AI with directly measurable business impact
  • High-velocity founding 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.

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Likely interview questions

  • Walk us through a production LLM feature you shipped end-to-end—what were the biggest challenges with evaluation and observability?
  • How have you approached building evals when ground truth is hard to define or noisy?
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