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
- SQL experience
- Data pipelines and analytical workflows
- Healthcare or clinical domain experience
- Experience building search, knowledge management, copilot, agent, or AI-powered applications
- Experience evaluating LLM quality, prompt design, or AI workflow optimization