About the role

Aplyr's Quick Take

This is a Full-Stack Engineering Intern role focused on building scalable systems for decision intelligence in manufacturing. You'll work independently on projects, engage directly with customers, and receive mentorship from experienced engineers. It's an individual contributor position with a hands-on approach to both backend and basic frontend development.

Good fit

Ideal candidates are those who have experience shipping production code and are comfortable with both backend infrastructure and basic frontend features. A strong product sense and a willingness to engage with customers are essential, along with a desire to learn and grow in a fast-paced environment.

Worth noting

The company emphasizes a no-nonsense culture with direct feedback, which may appeal to those who prefer straightforward communication. The role offers the chance to work on impactful projects with autonomy, but it requires being onsite in San Francisco.

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

Skills & Tags

react python typescript go ai frontend backend fullstack

Aplyr's read

Dryft is a tech innovator in digital communication, attracting engineers and designers eager to shape the future of collaboration tools.
Synthesized from recent postings & public sources

What's promising

•Dryft is at the forefront of digital communication innovation, offering employees a chance to impact the future of collaboration.
•The company hires for foundational roles, indicating opportunities for significant influence and career growth.
•Dryft's focus on engineering and design suggests a strong commitment to product development and user experience.

What to watch

•Limited public information about Dryft's market performance or customer base raises questions about stability.
•As a newer company, Dryft may face challenges in establishing brand recognition and trust.
•Potential employees might encounter resource constraints typical of early-stage companies.

Why Dryft

•Dryft emphasizes foundational roles, providing a unique opportunity to shape core aspects of the company.
•The company's focus on digital communication positions it uniquely in a rapidly evolving tech landscape.
•Dryft's commitment to innovative collaboration tools differentiates it from traditional tech companies.

Aplyr’s read is generated by AI from public sources. Was it useful?