Known - Conversational AI Engineer, System Prompt and Orchestration

San Francisco, CA (In-Person)

Known is a matchmaker that talks to users and supports them like a friend. Our mission is to empower humanity by applying general intelligence to human connection.

Users join Known by telling us their life story. On average, our new users talk to our AI voice agent for 27 minutes, giving us a uniquely intimate multi-modal data set.

We are a team of engineers who’ve created some of the most widely used AI-driven consumer products including Uber Eats, Uber, Faire and Afterpay.

We love to work hard, with a high degree of autonomy and ownership. We work together in Cow Hollow, San Francisco.

About the Role

We’re looking for founding Conversational AI Engineers to build the prompt systems powering our voice-led onboarding and user experiences.

This is a unique opportunity to work with a hyper-personalized data-set, combining voice transcripts, images, and structured user data to empower real-time, personalized AI voice-led conversations at scale. You’ll work directly with Chen Peng, former head of ML at Uber Eats and Faire.

What You’ll Do

Prompt Orchestration & Context Optimization: Architecting the core system prompts and managing context windows to ensure highly responsive, contextually relevant, and logically sound AI reasoning without bloating token counts or causing latency spikes.
EQ & Semantic Memory: Building prompt systems that allow Known to maintain a consistent, empathetic, and uniquely "Known" personality. You'll design mechanisms to seamlessly weave long-term user memories and preferences into real-time dialogue, while helping the user drive the conversation.
Conversational Intelligence: Designing advanced prompt chains (and fallback logic) to gracefully handle conversational tangents, user interruptions, semantic end-of-turn conversation logic,, and complex emotional states so Known feels empathetic and responsive.
Agentic Workflow Design: Implementing and maintaining the prompt-driven logic for multi-agent frameworks, where your system instructions act as the routing engine between the user, external APIs, and our internal matchmaking engine.
Evals for Conversational Quality: Developing custom evaluation frameworks to measure "conversational success." You'll go beyond basic fact-checking to rigorously assess conversational dynamism, warmth, engagement, and hallucination reduction.

Requirements

We’re looking for someone who can make automated systems feel undeniably natural:

2-3 Years in Conversational AI/NLP: Proven experience designing, testing, and deploying complex LLM applications and system prompts in high-traffic production environments.
The Prompt Stack: Deep familiarity with state-of-the-art prompt engineering techniques (e.g., Few-Shot, Chain-of-Thought, ReAct).
Agentic & RAG Architectures: Experience building the "brain" logic for LLMs using frameworks like LangGraph, LlamaIndex, or Haystack to manage complex, non-linear dialogue and dynamic knowledge retrieval.
Production Hardened: You treat prompts as an engineering problem. You’ve optimized prompt systems for scale, API cost, and speed. You're comfortable with prompt version control, programmatic prompt optimization (e.g., DSPy), and building continuous integration pipelines for AI evals.

Our Investors

We’re backed by Eurie Kim and Kirsten Green at Forerunner Ventures (the investors behind Decagon, Faire, and Oura), NFX and PearVC.

Learn more