Technical Product Builder (AI)

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 cur…