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Dryft

Full-Stack Engineering Intern

San FranciscofulltimeinternAdded 1 month ago

About this role

Dryft seeks a Full-Stack Engineering Intern to build intelligent decision-making systems that automate manufacturing operations. You'll work autonomously on dedicated projects combining agent-based reasoning, optimization, and simulation while collaborating with customers and learning from experienced founders and early-stage engineers.

What you'll do

  • Build scalable systems integrating agent-based reasoning with optimization and simulation
  • Develop end-to-end features across frontend and backend
  • Work directly with customers to understand and implement decision intelligence solutions
  • Contribute to infrastructure and architecture decisions
  • Ship production code with mentorship from senior engineers

What they're looking for

  • Full-stack development with TypeScript/React and Python/FastAPI
  • Backend infrastructure and database design
  • Production code shipping experience
  • Agent and AI system design
  • Product sense and customer collaboration
  • Data-driven systems and optimization

Benefits

  • Onsite position in San Francisco with visa support
  • Direct mentorship from experienced founders and early-stage engineers
  • High autonomy and responsibility on dedicated projects
  • Access to cutting-edge AI/ML tech stack (Pydantic AI, Langfuse, pgvector)
  • Opportunity to work with real manufacturing data and customers
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Dryft

Dryft builds AI-powered automation systems that use agent-based reasoning, optimization, and simulation to intelligently manage manufacturing operations and industrial data. The company is hiring founding engineers across full-stack development, design, and customer-deployed roles to establish its core platform and drive early customer success.

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Likely interview questions

  • Walk us through a project where you shipped production code end-to-end across backend, infrastructure, and frontend. What was your biggest technical challenge?
  • Describe your experience building or working with data-driven systems. How would you approach designing a scalable agent that reasons over large industrial datasets?
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