Product Engineer Intern

About Ditto

Ditto is the fastest growing college-facing dating platform reimagining the world of social connection. In just 3 months, we’ve 3x to over 120,000 users and are backed with $9.2m seed round by world-class investors, including Google and leading VC firms. We’re building a product that goes viral on campuses and brings authentic matchmaking to Gen Z and we’re just getting started.

About the Role

Ditto is building the agentic social network — where AI agents don't just assist users, they run the system: understanding people, making decisions, learning from outcomes, and continuously improving how humans meet in the real world.

This is not a traditional product engineering role.

We're looking for a product engineer who has shipped consumer products that people love — and who now wants to build products where AI is the execution layer. You'll own entire user-facing experiences end to end: from understanding what users need, to designing the right interaction, to building the system that delivers it autonomously.

You will help bring Ditto's matchmaking and engagement engine to life — building the experiences users touch and the infrastructure that makes those experiences feel magical. You'll collaborate closely with product, research, and infrastructure to shape the core of Ditto's platform.

In This Role, You Will

• Own end-to-end product experiences across matching, chat, scheduling, and re-engagement — from concept through shipping and iteration

• Make product decisions daily: what to build, how it should feel, when to ship, and what to measure

• Build agent-driven product flows where AI handles execution and you shape the experience

• Design and implement AI-orchestrated pipelines that replace manual workflows with autonomous ones

• Create internal tools for humans and AIs to inspect system state, debug agent behavior, evaluate outcomes, and steer system direction

• Implement feedback loops connecting user behavior, agent decisions, and real-world outcomes (matches, replies, dates, retention)

• Rapidly prototype, test, and iterate based on real user data — not assumptions

How You Will Work

You will operate as a product-minded manager of AI agents.

Your job is not to write every line of code — it is to:

• Identify what users actually need (often before they do)

• Define what agents should do to deliver that

• Design the tools and interfaces that make autonomous systems feel human

• Validate outputs against real user outcomes

• Build the infrastructure that lets the system learn from experience

You will continuously turn: User insight → Autonomous systems → Measurable outcomes

What We're Looking For

We want someone who thinks in user problems, systems, and feedback loops, not just features and tickets.

You should:

• Have shipped consumer products to real users and iterated based on what you learned

• Have strong product instincts. You can look at usage data, a conversation thread, or a user complaint and know what to build next

• Be comfortable across frontend, backend, and AI

• Understand stateful and autonomous systems

• Be excited by AI as the execution layer, not just an API

• Thrive in early-stage environments where scope is ambiguous and speed matters

Your background likely includes

• 2+ years building and shipping consumer-facing products (dating, social, messaging, marketplaces, or similar high-frequency consumer apps)

• Experience owning product outcomes, not just code — you've thought about activation, retention, and engagement, not just implementation

• Strong TypeScript and/or Python with modern web frameworks (React, Next.js, etc.)

• Experience building backend systems (Node, Bun, NestJS, FastAPI, or similar)

• Experience with stateful systems (Redis, MongoDB, or similar)

• Exposure to LLM pipelines, agents, or orchestration frameworks (LangGraph, LangChain, custom agents, etc.)

• Experience with A/B testing, experimentation, or growth loops

• A mindset of shipping fast, measuring real outcomes, and iterating based on data

Bonus (not required)

• Experience with event-driven or distributed systems (RabbitMQ, queues, workers)

• Experience building autonomous or AI-driven workflows

• Experience with observability, logging, and debugging of AI systems

• Experience with reinforcement learning, evaluation, or ranking systems