Technical AI Product Manager

Overview

About The Role Coin Market Cap  is building AI products to help users discover, understand, and act on crypto information more effectively. We’re hiring a Technical AI Product Manager to join our AI team. This is an individual contributor role in the Product org. You’ll spend most of your time building and iterating: identifying user pain points, prototyping in Python, prompt engineering, context engineering, and improving AI features we’ve already shipped based on real usage and feedback.

Once an MVP is validated, you’ll partner with Engineering to product ionize and scale it. You are not responsible for long-term platform scalability or owning the full engineering surface area. You are responsible for producing strong prototypes and handing Engineering a clear, validated direction.

Includes but not limited to:

• Identify the biggest user pain points where a crypto AI can materially improve outcomes
• Turn ambiguous ideas into a clear MVP, with crisp scope, constraints, and success metrics
• Prototype full AI experiences in Python to validate value and quality before we ship to production
• Own prompts and context engineering: instruction design, context shaping, guardrails, tool/function calling patterns, and output formatting
• Build practical evaluation loops: golden sets, scenario coverage, qualitative rubrics, regressions, and acceptance criteria
• Design the AI user experience: make it clear, trustworthy, and resilient if things go wrong
• Run fast experiments, learn from real outputs and usage data, and iterate quickly
• Partner with Engineering to ship: provide handoff specs, edge cases, evaluation results, and support debugging and iteration post-launch
• Work on whatever surface is the highest leverage

• Strong product judgment and the ability to make good calls under ambiguity
• Hands-on Python prototyping ability: you move fast, write clean code, and can translate ideas into working prototypes
• Practical LLM experience + intuition: you understand prompt iteration, context design, and have a strong intuition for how to build useful products on top of LLMs
• A strong evaluation mindset: you can define quality, test for failure modes, and prevent regressions without heavy process
• High-agency execution: you can go from “vague problem” → “shipped learning” with minimal supervision
• Excellent communication skills (verbal and written): convey complex messages clearly and simply, and drive conviction across stakeholders

• Shipped user-facing AI features (chat, agents, copilots, summarization, search/Q&A, personalization)
• 0 to 1 experience in fast-moving environments and owning ambiguous problems end-to-end
• Experience building tool-using and agent-like workflows
• Experience and interest in cryptocurrency