Member of Technical Staff

Ditto • San Francisco, CA

3d

About The Position

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 full-stack role. We are looking for engineers who want to build systems where AI is the execution layer and humans design, guide, and govern those systems. In this role, you will help bring Ditto’s autonomous matchmaking and engagement engine to life. You will build both customer-facing experiences powered by agents and the internal tooling that allows humans and AI to observe, debug, and improve those agents. You will collaborate closely with product, research, and infrastructure to shape the core of Ditto’s agentic platform.

Requirements

• Have built real production systems
• Be comfortable across frontend, backend, and AI
• Understand stateful and autonomous systems
• Be excited by AI as the execution layer, not just an API
• 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 event-driven or distributed systems (RabbitMQ, queues, workers)
• 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
• Experience building autonomous or AI-driven workflows
• Experience with observability, logging, and debugging of AI systems
• A mindset of shipping fast, measuring real outcomes, and iterating based on data

Nice To Haves

• Experience with reinforcement learning, evaluation, or ranking systems

Responsibilities

• Build agent-driven product flows across matching, chat, scheduling, and re-engagement
• Own customer-facing social experiences powered by autonomous AI systems
• Design and implement AI-orchestrated pipelines that replace manual workflows
• 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)
• Optimize the system for reliability, speed, and scale

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed