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Clera

Full Stack Software Engineer

New York (Remote)$120k–$200kfulltimemidAdded today

About this role

Join an early-stage AI/SaaS startup as a Full Stack Software Engineer where you'll own features end-to-end, building backend services in Go or Python and frontend interfaces in React/TypeScript while collaborating with ML engineers to ship AI-driven products to enterprise customers.

What you'll do

  • Build and deploy full-stack features using Go/Python backends and React/TypeScript frontends
  • Design APIs, data models, and service architectures for agentic AI capabilities
  • Own features through complete lifecycle: scoping, architecture, implementation, testing, deployment, and iteration
  • Create intuitive interfaces translating complex enterprise data into actionable workflows
  • Collaborate with ML engineers to productionize AI-driven features
  • Work directly with enterprise customers to understand needs and refine the product

What they're looking for

  • TypeScript and React
  • Go or Python backend development
  • AWS or equivalent cloud infrastructure
  • API and data model design
  • Data pipeline development and data engineering
  • Modern deployment practices
  • Full-stack ownership and end-to-end shipping
  • Product thinking and user-focused development

Benefits

  • End-to-end feature ownership and autonomy
  • Direct collaboration with enterprise customers
  • Early-stage startup environment with high impact
  • Exposure to AI and agentic systems
  • 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

  • Walk us through a full-stack feature you shipped end-to-end—what was your architecture decision and why?
  • Tell us about a time you worked on a data pipeline or data engineering project. What tools did you use and what challenges did you face?
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