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Clera

AI Applied Engineer

New York (Remote)$180k–$250kfulltimemidAdded today

About this role

Join a seed-stage AI fintech compliance startup as an early AI Applied Engineer to design and deploy a multi-agent platform for identity data processing and KYB compliance. You'll shape the AI roadmap alongside the founding team, directly engage with customers, and build evaluation frameworks that drive real-world product impact.

What you'll do

  • Define AI strategy and influence product roadmap with executive leadership
  • Design and develop multi-agent AI systems for identity and compliance data processing
  • Create evaluation frameworks and benchmarks to measure agent performance
  • Gather customer requirements and translate feedback into product improvements
  • Research latest AI/ML developments and translate findings into tangible features
  • Foster collaborative engineering culture as a founding team member

What they're looking for

  • Python development for AI/ML workflows
  • Multi-agent AI platform architecture
  • Large language models (LLMs)
  • Machine learning pipelines
  • Data integration and identity data reconciliation
  • Evaluation and benchmarking frameworks
  • Customer engagement and requirements gathering
  • Early-stage startup experience

Benefits

  • Early-stage equity opportunity
  • Direct influence on product strategy and roadmap
  • Work directly with CEO and CTO
  • On-site position in New York
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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 your experience building and deploying multi-agent AI systems—what were the key challenges and how did you evaluate their performance?
  • Tell us about your most impactful work with LLMs and machine learning pipelines. How did you measure success?
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