Live opening · Posted 27 days ago
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Responsibilities:
Own the AI agent problem decomposition: Deeply understand LLM-based agents in healthcare. Deconstruct complex clinical research into well-defined agent tasks. Define reliability, evaluation metrics, run evals, and iterate on results end-to-end.
Go deep on oncology data and problem formulation: Gain a deep understanding of cancer patient data (pathology, treatment, genomics, clinical notes). Collaborate closely with the NLP team to define precise, clinically data-grounded problem statements.
Build prototypes and evaluation tools: Prototype and build POCs using tools like Cursor/Claude Code. Design internal apps to evaluate AI model outputs. Coordinate with data annotation for quality, and work with the AI team for model improvements.
Bridge AI capabilities to product workflows: Collaborate with product managers to integrate AI into user-facing workflows. Leverage a deep understanding of AI agent capabilities to reimagine workflows and design ideal, genuinely AI-native user experiences, moving beyond merely 'bolted-on' AI.
Own the feedback loop between annotation, evaluation, and improvement: Oversee the full AI lifecycle: define annotation, ensure output quality, evaluate, identify failure modes, and drive mitigation with AI engineering. You are accountable for model reliability in production, not just demos.
Requirements:
4+ years in product management with at least 1.5 years working directly on AI/ML products.
Hands-on experience with AI engineering concepts: RAG pipelines, agents, MCPs, LLM APIs, and LLM evaluation frameworks.
Ability to vibe code, comfortable using Cursor, Copilot, or similar tools to build quick prototypes, evaluation apps, and POCs independently.
Experience formulating ML/NLP problem statements from messy, real-world data
Strong understanding of how LLM-based agents work, how to decompose problems into agent architectures, and how to evaluate their reliability.
Excellent communication skills with the ability to operate across AI engineering, product, design, and data annotation teams.
Self-driven, with a bias toward action. You don't wait for permission to prototype, investigate, or push things forward.
Experience
3-7 yrs
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