ashby
AI / ML Engineer - Known
Pear VC
Remote, USA
Full-time
Mid · 3+ years experience
Remote
Discovered 1 weeks ago
PythonPyTorchTensorFlowJAXHugging Face
Free
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Role Overview
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.
Key Skills for This Role
PythonPyTorchTensorFlowJAXHugging Face
Full Job Posting
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.
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