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Clera

Full Stack Software Engineer

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

About this role

Join an early-stage AI-native consumer intelligence platform as a full stack engineer, owning features end-to-end from Go/Python backend services to React frontends. You'll work with enterprise customers and ML engineers in a fast-moving team to ship data-driven products that help brands understand consumers.

What you'll do

  • Build full-stack features using Go/Python backends and React/TypeScript frontends
  • Design APIs, data models, and service architectures for agentic AI capabilities
  • Create intuitive interfaces that translate complex enterprise data into actionable workflows
  • Collaborate with ML engineers to move AI features from prototype to production
  • Own features through scoping, architecture, implementation, testing, deployment, and iteration
  • Work directly with enterprise customers to understand needs and refine product

What they're looking for

  • TypeScript/React
  • Go or Python
  • AWS or equivalent cloud infrastructure
  • Data pipelines and data engineering
  • API and data model design
  • Product thinking and user-centric design
  • Full-stack ownership and ambiguous problem-solving
  • LLM integrations or agentic AI systems (bonus)

Benefits

  • Base salary $120,000 – $200,000 USD annually
  • On-site in New York, NY
  • Work on AI-native consumer intelligence platform
  • Direct customer interaction and product impact
  • Early-stage startup environment with autonomy
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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

  • Can you describe a full-stack feature you shipped end-to-end, and how you balanced backend and frontend concerns?
  • How have you approached learning or working with LLMs or agentic AI systems in production?
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