Founding AI Engineer

We’re hiring an AI Engineer to build the intelligence layer for the leading AI companion for language learning. You’ll own the core AI systems powering personalized conversations for millions of users.

Language learning shouldn’t feel like an app — it should feel like talking to someone who knows you: remembers what you struggled with last week, adapts in real time, and responds instantly.

Building that means solving hard systems problems at consumer scale: long-term memory, real-time speech, sub-second latency, adaptive dialogue, and personalized content.

About Pingo

Pingo helps 8,000,000+ users build fluency through real conversations. Since launching in January 2025, we’ve grown to $12 million ARR with ~50% MoM growth. Backed by Y Combinator and top investors, we’re building a small, talent-dense team obsessed with product quality and engineering rigor.

What You’ll Do

In one sentence: Build the systems that make Pingo the default way people learn languages.

• Define and own our AI architecture and roadmap, and build the AI team as we scale.

• Architect memory & personalization — design scalable long-term memory and retrieval, build a content engine that generates structured, high-novelty conversations.

• Improve speech & conversation intelligence — optimize ASR feedback, turn-taking, and dialogue flow to make interactions feel natural.

• Own AI evals & experimentation system — build prompt simulations, golden datasets, and regression systems to measure conversation quality.

• Optimize real-time performance — drive sub-200ms latency and systems that scale efficiently across millions of conversations.

• Tie intelligence directly to retention — build systems and metrics that measurably improve D1/D7/D30 engagement.

You might be a great fit if you:

• Think in systems. Understand how memory, latency, evaluation, and UX interact.

• Set a high engineering bar — nothing ships broken.

• Strong product judgement — you build features that feel instant and magical.

• Run rigorous experiments and measure tradeoffs across quality, latency, and cost.

• Detail-oriented & obsessed with quality – care deeply about stability, polish, and learning impact.