Senior NLP Engineer (Agentic AI)

Ditto AI is building the agentic social network — and we are doing it as an AI-native company.

We’re not trying to replace human interaction. We’re trying to remove friction, reduce noise, and make introductions and connections more efficient, thoughtful, effective.

That requires solid engineering and pragmatic NLP work — not demos.

Role Overview

We’re hiring a Senior NLP Engineer (Agentic AI) to help design and build the language systems that power our agents.

You will work across model integration, conversation pipelines, agent orchestration, and language governance — with a focus on production reliability. Every profile runs as an AI agent that:

• understands people beyond surface signals

• uncovers hidden compatibility patterns

• reduces noise and guessing

• helps people connect smarter, not louder

This role is heavily hands-on. You’ll be building, shipping, and iterating — not just prototyping.

What You’ll Build

• conversational systems that hold up under real usage

• retrieval pipelines where accuracy actually matters

• enrichment layers that structure messy data

• durable context + memory systems

• matchmaking and chatting logic that reveals hidden compatibility

• runtime safety and observability

• evaluation tied to real, measurable outcomes

• learning loops that improve — without losing control

Everything must be observable, testable, and maintainable.

Must-Have Experience

We’re looking for an experienced builder.

• 7+ years of hands-on NLP engineering in production

• 2+ years building LLM-enabled systems in production

• shipped conversational AI / multi-turn agent systems

• LangGraph and/or LangChain used in production, not just prototypes

• Proficient in TypeScript and Python

• proficient using AI coding tools like Cursor and Claude Code

• Retrieval systems + AI database using:

• MongoDB

• Redis

• AI DBs — ideally Weaviate

Nice to Have

• social / dating / recommendation platforms

• trust & safety or moderation systems

• Reinforcement learning — especially TD learning (super plus)

How We Work

We value engineers who:

• think in systems, not just features

• ship iteratively and measure results

• design for failure modes, not just best cases

• care about how systems behave in production

• take ownership from design through deployment