Principle Product Builder

Principle Product Builder at DocNow About the job About DocNow DocNow is the EHR by physicians, for physicians for post-acute care — serving the SNFs, LTACs, and nursing homes where PM&R, wound care, and podiatry providers do their rounds. We’re rebuilding our platform from the ground up as an AI-native EHR — and we’re rebuilding how product gets made at the same time. The role We are not hiring a classic product manager to groom a backlog and write tickets. That job is shrinking, fast. We’re hiring a hands-on product builder who uses AI to design, spec, and prototype product at a speed that used to take a whole team — writing specifications rigorous enough that an AI-assisted engineering team can build against them, and keeping those specs in the codebase as the source of truth. You won’t write the production code — our engineers and their coding agents do that, and they own architecture and engineering practices. You’ll define and shape the product itself, hands-on, with the rigor and velocity AI now makes possible. If your instinct on hearing “the product spec is a markdown file in Git that the build pipeline runs against” is “yes, finally” — keep reading. What you’ll do Define and spec product, AI-natively. Use AI to research, design, and write specifications precise enough for an AI-assisted engineering team to build against — kept in Git as the source of truth (spec-driven development, from the product side). Prototype with AI to pressure-test and de-risk ideas before they enter the build pipeline — clickable flows, mockups, proofs of concept. Own prioritization across three tracks — the AI-native rebuild, legacy-platform maintenance for current customers, and turning AI research into shipped product — and personally push the highest-leverage work. Productize AI. Our Innovation team builds AI prototypes; you decide which become real product and drive them to launch with engineering. Be engineering’s sharpest partner. Fluent enough in how AI-assisted development works — and technical enough to work in Git and read the codebase — to write specs the team and their agents can execute cleanly, and to make smart scope and sequencing calls. Engineering writes the production code and owns architecture and dev practices; you own the product. Carry the customer into the product. Translate the needs of a concentrated, high-stakes customer base into what ships — without becoming an order-taker. Own the product side of migration onto the new platform without disrupting clinical and billing workflows. This starts as a builder role, not a manager role. There’s room to grow and lead a small product team over time, but that’s not the requirement. What success looks like 90 days: Defining and shipping product through the AI-native workflow — specs live in Git, prototypes validate ideas before the build, first features are out. 6 months: Product is no longer the bottleneck on the rebuild — definition and validation land ahead of engineering, not behind it. The first AI prototypes are productized and live. 6–12 months: A repeatable AI-native product process the rest of the org can work in; a credible customer migration underway. What we’re looking for A hands-on product builder, not a backlog manager — a track record of shipping product you personally researched, specced, and prototyped, at pace. Works AI-native today — uses AI in the actual job day-to-day to do product work (research, specs, prototypes, design), not conceptually. Comfortable with spec-driven development and product specs living in Git. Familiarity with frameworks like BMAD, OpenSpec, GitHub spec-kit, or similar is a strong plus — this is new enough that we don’t expect deep tenure, but if you haven’t used them you must be technical enough to learn them fast and work this way from day one. Technically fluent, but not a developer. Comfortable in Git, able to write specs precise enough for AI agents and engineers to build against, and to read the codebase well enough to be a sharp partner. You won’t write production code or own engineering practices — we have a CTO and architect for that. Judgment under constraint — can prioritize ruthlessly across a new build vs. legacy upkeep vs. AI, with a concentrated customer base where the stakes per account are high. Clear communicator — equally credible with engineers, clinicians, and executives. A plus, not a gate: post-acute care (SNF/LTAC) or adjacent experience (PM&R, wound care, podiatry); compliance fluency (MIPS, PDPM, HIPAA); healthcare integrations (PointClickCare, MatrixCare); hands-on with AI/ML-embedded products (NLP, ambient/voice documentation, clinical automation); MBA or work experience in healthcare services or technology industry. How we work DocNow was built by providers to fix what existing EHRs got wrong in post-acute care. An EHR is not a weekend prototype — it’s regulated, clinical, billing-critical software, so engineering rigor matters more here, not less. Going AI-native is how we move with unusual speed and hold that rigor — it’s not a license to cut corners. We’re betting that a small, high-leverage team working this way out-builds much larger ones. You’ll join a lean team where your work ships. If you want to define and shape product with the newest tools and own the outcome, this is that seat.