Conversational AI Engineer, System Prompt
KnownDating / company
San Francisco, United StatesMid
Forerunner Ventures
NFX
Adverb Ventures
Coelius Capital
Pear (California)
Data & AI
About the role
TL;DR
Build prompt systems for voice-led onboarding and user experiences.
- •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.
- •Key Responsibilities 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 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.
Required skills
PythonNLPLLMsTensorFlowPyTorchscikit-learnLangChainHugging FacePandasNumPyAirflowPostgreSQLMySQLMongoDBRedis
Domain expertise
fintech
Tech stack
PythonTensorFlowPyTorchscikit-learnLangChainHugging FaceNLPLLMsPandasNumPyAirflowPostgreSQLMySQLMongoDBRedisAWSDockerKubernetesCI/CDGitHub ActionsGitJiraConfluencePostmanREST APIJestPytest