AI Technical Product Builder

Role Summary We're building AI-powered search and knowledge systems that help clinical users find the right information fast. We need a builder who can take ambiguous problems, work directly with users and stakeholders, and deliver working solutions using AI-assisted development tools as a core part of their workflow. This role sits at the intersection of Product Management, Engineering, and AI-enabled development. The ideal candidate combines product judgment, technical fluency, and hands-on execution to move products from concept to adoption. This is not a traditional Product Owner role and not a traditional Software Engineer role. We're looking for someone who can bridge both worlds. What Success Looks Like The successful candidate will: Take products from ambiguous concepts to shipped solutions.Scale existing products from early adoption to enterprise-wide usage.Work directly with users and business stakeholders to uncover needs.Utilize AI coding tools to accelerate delivery while maintaining quality.Exercise strong judgment about what to build, what not to build, and when solutions are ready for release.Collaborate closely with engineers while maintaining ownership of product outcomes. Who We're Looking For A hands-on builder with strong product instincts and technical fluency. Someone who has personally shipped software end-to-end and understands both how products are built and why users adopt them. They should be comfortable using AI coding tools (Claude Code, Cursor, Codex, or similar) as a daily part of how they work, while remaining accountable for every solution they deliver. This is a builder role, not a specification-execution role. The person will own meaningful portions of products with limited supervision and must demonstrate the judgment to make independent decisions while maintaining a high quality bar. Must-Haves Product Ownership & Agency Demonstrated ability to own ambiguous problems from discovery through delivery.High agency and self-direction.Ability to independently drive initiatives, make decisions, and deliver outcomes.Proven experience taking products from 0→1 and helping scale them from 1→100.Full-Stack Builder Mindset Personally built and delivered software products across frontend, backend, and data layers.Stack-agnostic; focused on outcomes rather than specific technologies.Comfortable moving between product discussions and technical implementation.AI-Assisted Development Experience Regular, sustained use of AI coding tools such as Claude Code, Cursor, Codex, or similar.Ability to demonstrate recent work built using AI-assisted workflows.Understands both the capabilities and limitations of AI-generated solutions.Technical Judgment Able to review, evaluate, and defend AI-generated implementations.Does not blindly accept AI output.Understands what was built, why it was built, and the tradeoffs involved.Can identify flaws, risks, or implementation concerns before solutions reach engineering review.User & Stakeholder Engagement Direct experience working with users and business stakeholders.Comfortable engaging with non-technical audiences.Ability to translate business problems into product requirements and working solutions.Experience interacting with leads, managers, directors, or equivalent stakeholders.Data-Driven Product Thinking Uses metrics and user feedback to evaluate success.Understands how to measure adoption, usage, engagement, and product impact.Makes prioritization decisions based on evidence, not assumptions. How the Role Works With Engineering The successful candidate: Partners closely with engineering on architecture and implementation decisions.Builds within established engineering workflows, repositories, pull requests, and code review processes.Owns solutions through review, testing, and release.Can explain, defend, and modify code generated through AI-assisted workflows.Understands enough about implementation to engage meaningfully in technical discussions. Strong Indicators of Success Candidates who stand out will often have: AI-powered side projects.Personal products or applications they've built and shipped.Demonstrable portfolios showing experimentation and delivery.Examples of using AI tools to accelerate execution while maintaining quality.Evidence of continuous learning and curiosity around emerging AI technologies. Nice-to-Haves Python experienceSQL experienceData pipelines and analytical workflowsHealthcare or clinical domain experienceExperience building search, knowledge management, copilot, agent, or AI-powered applicationsExperience evaluating LLM quality, prompt design, or AI workflow optimization