AI Product Manager

Machinify is a leading healthcare intelligence company focused on delivering value and efficiency to health plan clients. The AI Product Manager will define product strategy, engage deeply in system design, and leverage AI tools to enhance product development and drive outcomes through influence across cross-functional teams.

Responsibilities:

• Own Strategy at the System Level
• Define product strategy that accounts for technical constraints and unlocks — not one that ignores the stack or hides behind abstraction
• Translate business objectives into technical product bets that engineering teams can execute with confidence
• Drive discovery at the API, data-model, and architecture level — understand what's achievable before writing a requirements doc
• Own metrics and instrument what matters. Drive decisions from data and system telemetry, not assumption
• Engage at the level of system design: read specs, evaluate tradeoffs, push back on decisions that create debt or constrain future options
• Write PRDs that engineers don't need to translate — precise, unambiguous, grounded in technical reality. Better yet: hand them a blueprint built from live prototype sessions with real users, not from assumption
• Vibe-code working prototypes and deploy them to real users, SMEs, and domain experts before production engineering touches the problem — run live lab tests, not focus groups, and harvest the behavioral dataset that turns engineering into blueprint execution, not exploration
• Decompose complex systems into shippable increments without losing the user story thread
• Eliminate the friction that blocks great engineering work. Protect team focus — and protect it by arriving with answers, not questions
• Build and maintain a personal AI toolkit for research synthesis, discovery automation, spec writing, and rapid iteration
• Vibe-code working prototypes using AI coding environments — not wireframes, not mockups, functional products — and deploy them to end users, SMEs, and domain experts. Run live lab tests, collect comprehensive behavioral data, and harvest edge-case signal before production engineering starts. The output isn't a validated spec; it's a dataset. Production builds from it at speed, with zero ambiguity and minimal post-ship rework
• Use AI agents to accelerate every phase: user research, competitive analysis, data analysis, documentation
• Drive AI integration where it removes real friction — not where it just adds a chatbot to something that didn't need one
• Align stakeholders through demonstrated insight and momentum — not status reports
• Build the case for the right technical investments at the executive level, grounded in business outcomes
• Navigate cross-functional complexity — eng, design, data, infra, security — without creating coordination overhead
• Know when to move fast and when to slow down to get alignment right

Requirements:

• 7+ years building technical products — APIs, platforms, data products, infrastructure-adjacent experiences — with a track record of shipping things that changed behavior
• Background that includes engineering, data engineering, or systems design — enough to have made real technical tradeoffs under pressure
• Genuine fluency with AI tooling: LLMs, agent frameworks, AI-assisted development and research environments
• Experience partnering with platform, infrastructure, or data engineering teams. Comfort with systems that don't have a clean UI
• Operated with high autonomy in ambiguous environments — takes initiative before being asked, closes loops without prompting
• Communicates with precision. Doesn't need a slide deck to make a point