Pear VC

AI / ML Engineer - Known

Pear VC
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1 year ago

Responsibilities

  • Design and implement multi-stage matching systems using embedding-based retrieval and LLM re-ranking for compatibility scoring, search, and personalization.
  • Develop and maintain pipelines for data ingestion, feature generation, model training, evaluation, and inference.
  • Prototype and productionize agentic workflows for natural-language and voice interactions, including intake interviews and conversational matchmaking.
  • Deploy and monitor production ML models with performance, fairness, and safety guardrails.
  • Run offline and online A/B and multivariate experiments measuring engagement, match success, and conversation quality.
  • Build profile enrichment, model evaluation, monitoring, and retraining systems informed by live interaction feedback.
  • Collaborate with platform engineers, product designers, and the founding team to integrate AI into the Known user experience.

Requirements

  • At least 3 years of experience in applied ML or data science engineering, ideally involving recommendation, search, or personalization systems.
  • Strong proficiency in Python and modern ML frameworks including PyTorch, TensorFlow, JAX, and Hugging Face.
  • Experience with LLMs, embeddings, and agentic workflows.
  • Understanding of A/B testing and human-in-the-loop system design for production model evaluation.
  • Familiarity with approximate nearest neighbor search systems and modern MLOps tools is preferred.
  • Reinforcement learning or preference modeling experience is strongly preferred.
  • Commitment to building safe, fair, and human-centered AI experiences.

Tech Stack

PythonPyTorchTensorFlow
Pear VC

About Pear VC

51-200 employees

Pear VC is an early-stage venture capital partnership that invests at pre-seed and seed, pairing capital with hands-on support, mentorship, and programs like its PearX accelerator for founding teams. Founded in 2013 and headquartered in Menlo Park, California, it backs software and technology startups across categories. Its portfolio includes companies that reached the public markets, such as DoorDash and Guardant Health.

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