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Clera

Technical Engineer

San FranciscofulltimemidAdded today

About this role

Join a venture-backed AI SaaS startup as a founding-level Technical Engineer to build and scale an industrial B2B marketplace platform. You'll own full-stack development from intelligent data pipelines and AI/ML systems to marketplace infrastructure, working directly with the founding team in a high-ownership, fast-moving environment.

What you'll do

  • Build and scale the technical backbone of an industrial B2B marketplace platform
  • Design and maintain data pipelines using Dagster or similar tools for thousands of data sources
  • Implement DevOps best practices and manage AWS cloud infrastructure with CloudFormation
  • Develop AI/ML integrations and agent workflows to automate industrial sales and distribution
  • Deliver full-stack features using Next.js/Vercel frontend and Supabase backend
  • Own end-to-end project delivery enabling large-scale transaction volumes

What they're looking for

  • Full-stack development (React/Next.js, TypeScript, backend APIs)
  • ML engineering and data pipeline architecture
  • AWS and infrastructure-as-code (CloudFormation, Terraform)
  • AI agent and LLM integration
  • DevOps and cloud operations
  • REST/GraphQL API design
  • Data orchestration tools (Dagster, Airflow)
  • Marketplace or e-commerce platform experience

Benefits

  • Meaningful equity upside as an early-stage founding member
  • High-impact technical ownership and influence on product strategy
  • Visa sponsorship available
  • Two-week paid work trial during interview process
  • Fast-paced startup environment with direct founding team collaboration
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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 production ML system you built end-to-end—how did you handle data pipelines, model deployment, and monitoring?
  • Tell us about a time you shipped a feature quickly under pressure while maintaining reliability—what trade-offs did you make?
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