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Full-Stack Engineering Intern

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Overview

Schedule
Full-time
Career level
Senior-level
Remote
On-site
Benefits
Career Development

Job Description

DRYFT

Dryft builds intelligent systems that automate complex human decisions in manufacturing operations

We’re a small, talent-dense team with modest roots and a love for the physical world - cars, machines, planes. Our team includes graduates from Stanford, Berkeley, MIT, TUM, winners of Stanford TreeHacks, and builders of everything from robots and race cars to software with millions of downloads.

We’re backed by the founders and leaders behind Amazon, Facebook, Stripe, Applied Intuition, & others; General Catalyst & Neo, and board members of global industrial companies.

We’re a no-bullshit, direct-feedback kind of team. People come to Dryft because they want to grow fast, with real responsibility.

THE ROLE

As a Fullstack Engineering Intern, you’ll be the brain behind Dryft’s decision intelligence. You’ll build scalable systems that combine agent-based reasoning with mathematical optimization and simulation on top of large-scale industrial data.

You will work autonomously on your own dedicated projects, often directly with the customer while receiving mentorship from senior engineers that work directly with you. Our early engineers were prior founding/early engineers at venture-backed startups in SF.

YOU

  • Like building scalable, interpretable, data-driven agents from all perspectives - algorithms, architecture and infrastructure.

  • Have a strong product sense and enjoy working directly with customers daily

  • Have shipped production code end-to-end, are strong in backend & infra and comfortable shipping basic frontend features.

  • Are ready to work onsite in SF (we support visa)

TECH STACK

  • FE (TypeScript): React, Chakra UI v3, TanStack Router, TanStack Query, Vite

  • BE (Python): FastAPI with Pydantic v2, SQLModel, Postgres with pgvector, dagster

  • AI: Pydantic AI, Langfuse

Automate your job search with Sonara.

Submit 10x as many applications with less effort than one manual application.

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