Clera
Founding AI Engineer
About this role
Join an early-stage B2B SaaS startup as a founding AI engineer to build the evaluation systems, LLM infrastructure, and agent-driven features for an AI-powered pricing platform. You'll own the critical path connecting model outputs to revenue impact, working in a lean team where shipping and measurable business outcomes define success.
What you'll do
- Design and build evaluation harnesses using real pricing outcomes as ground truth for continuous model improvement
- Automate expert review workflows and develop AI personas simulating B2B buying committees from usage data and transcripts
- Implement LLM routing across providers with explicit optimization for cost, latency, and quality trade-offs
- Extend MCP servers for LLM agents and ensure product features are agent-driven with full auditability
- Maintain data residency compliance and infrastructure boundaries (e.g., EU-only model routing)
- Identify and resolve latency, data drift, and cold-start issues in the pricing decision loop
What they're looking for
- LLM systems and production deployment (8+ years engineering, recent hands-on experience)
- Evaluation frameworks and benchmarking for AI systems
- LLM infrastructure and multi-model routing
- Typed data models and structured ontologies (Pydantic)
- MCP (Model Context Protocol) and LLM agent integration
- Data residency and compliance (GDPR, SOC2)
- Technical communication to non-technical stakeholders
- Startup agility and high-ownership mindset
Benefits
- Founding team equity opportunity
- High-impact role with direct influence on product and technical direction
- Remote-flexible work arrangement (based in Amsterdam)
- Small, product-focused team environment
- Work on revenue-critical AI infrastructure
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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
- Walk us through a production LLM system you shipped end-to-end—what was the eval strategy and how did you gate releases?
- Describe a time you optimized across cost, latency, and quality for LLM inference. How did you measure trade-offs?