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Clera

Founding Product Engineer

remote (Remote)$75k–$110kfulltimemidAdded today

About this role

Join a 2023-founded RegTech startup as a founding Product Engineer to build an AI-powered platform automating AML/KYC workflows for banks and fintechs. You'll own features end-to-end, work directly with enterprise customers on financial compliance problems, and shape the technical direction alongside the founding team using LLMs, Node.js, Python, and AWS.

What you'll do

  • Build and scale core platform features and zero-to-one initiatives using LLMs for compliance automation
  • Own features end-to-end: ship production code, operate systems, and iterate based on customer feedback
  • Translate LLM capabilities into reliable, understandable user experiences and structure non-deterministic outputs
  • Develop integrations, data infrastructure, and schemas enabling AI agents to automate compliance workflows
  • Engage directly with enterprise financial crime and risk teams to solve critical compliance problems
  • Mentor future teammates and help mature engineering practices as the team grows

What they're looking for

  • Node.js and TypeScript
  • Python
  • React
  • AWS Serverless
  • Large Language Models (LLMs) and NLP application
  • Data infrastructure and schema design
  • Enterprise software and SaaS development
  • Customer communication and on-call ownership

Benefits

  • Equity stake of 0.25–0.5% at early-stage company
  • Shape engineering culture and hiring from the ground up
  • Direct influence on technical and product strategy
  • Exposure to cutting-edge AI/LLM applications in regulated industries
  • Collaborative founding team environment
  • Remote-flexible, London-based hybrid role
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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 shipped a production feature end-to-end at an early-stage startup—what was hardest about the full ownership cycle?
  • Walk us through a project where you structured outputs from an LLM or NLP model for reliable downstream use. What were the key challenges?
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