AI / ML Engineer - Known

Job description

# **About the Role** You’ll be the technical founder driving the machine learning and AI backbone behind **Known** — an intelligent, compatibility-driven dating platform that blends psychology, data, and human-like conversation. You’ll design and ship the systems that make Known feel *magical*: personalized matching algorithms, adaptive recommendation loops, and natural voice/LLM-based interactions that help users connect meaningfully. You’ll work closely with the founding team (product, platform, and design) to shape both the **data and ML foundations** and the **user-facing experiences** that differentiate Known. This is a hands-on role with ownership across research, prototyping, and production deployment. # **Responsibilities** - **Design and implement** multi-stage matching systems (embedding-based retrieval + LLM re-ranking) for compatibility scoring, search, and personalization. - **Develop and maintain ML pipelines** for data ingestion, feature generation, model training, evaluation, and inference. - **Prototype and productionize agentic workflows** for natural-language and voice interactions (e.g., AI-assisted intake interviews, voice matching, or conversation agents). - **Deploy and monitor ML models** in production with guardrails for performance, fairness, and safety. - **Run offline & online experiments** (A/B and multivariate) to measure real-world outcomes such as engagement, match success rate, and conversation quality. - **Collaborate cross-functionally** with platform engineers and product designers to integrate AI seamlessly into the Known user experience. # **Requirements** - 3+ years in applied ML or data science engineering roles, ideally working on recommendation, search, or personalization systems. - Strong proficiency in **Python** and modern ML frameworks (PyTorch, TensorFlow, JAX, Hugging Face). - Experience with **LLMs, embeddings, and agentic workflows**. - Understanding of **A/B testing and human-in-the-loop system design** for model evaluation in production. - Familiarity with **ANN search systems** and modern MLOps tools is a plus. - Reinforcement learning or preference modeling experience is a strong plus. - You care about building **safe, fair, and human-centered AI** experiences. # **Example Projects** - Develop a user matching system based on profile information, onboarding transcripts and engagement behavior. - Build a dynamic profile enrichment pipeline that integrates behavioral and linguistic features into user representations. - Deploy a lightweight LLM-powered voice agent for user intake and conversational matchmaking. - Create an evaluation harness combining offline metrics (AUC, NDCG) and online experiments (match acceptance, message rate). - Build model monitoring and retraining loops informed by live interaction feedback. # **Why This Role** This is an opportunity to define the technical DNA of a consumer AI product from day one — to architect and deploy systems that combine **data science, human psychology, and generative AI**. Your work will directly shape how people connect, communicate, and build relationships in an AI-assisted world.