Ditto→
Product Engineer Intern at Ditto in San Francisco, CA
Skills
Job Description
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