Product Builder

Brooksource; Minneapolis (MN); Hybrid; USD 243,559,680 - 257,886,720; Full time

Job summary

Brooksource is seeking an engineer for our AI Innovation Office, a hybrid role within a large U.S. health services organization.

You will work on an AI-native R&D team building products that affect real healthcare outcomes—from detection to decision support—and you will ship usable solutions in collaboration with clinicians and data scientists. The role emphasizes product and business judgment, full-stack capability, and the ability to navigate stakeholders.

Qualifications

• Shipped products with real users across platforms.
• Full-stack technical capability across data, API, and UI.
• Product and business judgment in practice with evidence.
• Comfortable in ambiguity and shipping with incomplete specs.
• AI tool fluency in practice and demonstrated outcomes.
• Stakeholder-facing work and cross-functional collaboration.
• Learned new domains quickly and delivered impact.

Responsibilities

• Identify high-value problems and justify what to build.
• Build across the full stack when needed.
• Navigate stakeholders and deliver value to users.
• Ship products with real users and measure outcomes.
• Lead with product and business judgment as appropriate.

Skills

Product and business judgment

Technical depth

Stakeholder instincts

Full-stack capability

UX design sense

AI tool fluency in practice

Ownership mindset

Job description

Team: AI Innovation Office, within a top-5 U.S. health services organization

Hourly Rate: $85-$90 an hour

**This is W-2 with Brooksource. We are not able to provide sponsorship at this time, and we are not able to work c2c during the contract period.

The Role

We're a small, AI-native R&D team that builds products affecting real healthcare outcomes — how risk gets detected, how care decisions get made, how clinical teams do their work. We're looking for hybrid builders who figure out what to build, not just how — then ship it.

• Product and business judgment — Identifies the highest-value problems, aligns stakeholders, and makes the case for what to build before writing a line of code
• Technical depth — Can build it across the full stack once the problem is clear
• Stakeholder instincts — Navigates an organization, earns trust, and delivers value to the people who need it

The best builders on this team carry all three — and know when to lead with each.

The Problems We Work On

Turning overwhelming data into clarity. Large organizations generate more signal than any team can manually process. We build systems that find what matters, understand why it matters, and communicate it to decision-makers — in near-real-time, not weeks later. The technical challenge spans anomaly detection, causal reasoning, and natural language synthesis. The harder challenge: making AI-generated insight compelling enough that people actually change what they do.

Clinical AI that reaches real patients. We build tools that help care teams prioritize who needs attention and when — turning clinical data into actionable signals for the people making care decisions. The data is messy, the stakes are real, and getting it wrong matters.

Rapid prototyping that drives decisions. New AI opportunities surface constantly. We run short, high-intensity build cycles: from 'what if we could...' to a working prototype that moves a real decision. Scoping what to build is harder than building it — and this is where product and business judgment matters most.

Making the team itself faster. We build internal AI-augmented workflows that compound over time — tools that reduce overhead, accelerate research, and keep the team in flow on the hard problems. We use everything we build before it goes anywhere else.

What We Look For

Dimension

What It Looks Like

Product and business judgment

Thinks about users and value first. Asks "why this problem?" before "how do we solve it?" Has opinions about what to build — and what not to.

Full-stack capable. Moves from data to API to UI as the problem demands. Writes production-quality code.

Creates usable interfaces without a designer in the room. Knows good UX when they see it.

AI nativity

Uses AI tools as core infrastructure — coding, research, validation. Understands where models are strong and where they break.

Track record of finishing things. Ships products, not just pull requests.

Ownership mindset

Owns outcomes, not tasks. Takes initiative without waiting to be told.

We want builders who are deep in one or two areas and fluent across many — able to touch any part of the stack when the problem demands it.

Strong Candidate Signals

We evaluate on outputs, not inputs. What you've shipped tells us more than credentials or tenure.

Must-haves:

• Shipped products with real users — day job, open source, or personal projects. What was built, who used it, and what it changed.
• Full-stack technical capability — not just frontend, not just data. The whole system when the problem required it.
• Product and business judgment in practice — evidence of deciding what to build, not just executing on someone else's spec. Framed problems, aligned stakeholders, made the case.
• Comfortable in ambiguity — made decisions and shipped without a complete spec.
• AI tool fluency in practice — uses these tools daily, knows where they're good, and can demonstrate how.
• Outcomes over activities — "reduced X by Y%" over "built system using Z."
• Stakeholder-facing work — presented technical work to non-technical audiences, translated business needs into working software.
• Learned new domains quickly — worked across meaningfully different problem spaces and shipped in them. The path doesn't matter; the pattern of learning fast does.
• Design eye — built interfaces that users actually wanted to use, without a designer making every decision.

How We Work

• Spec-driven, not sprint-driven. Write a lightweight spec, build it, ship it, demo it. Minimal process overhead.
• High autonomy. You own workstreams, not tickets. The team has real input into what problems to pursue.
• AI-native every day. AI-assisted development is how this team operates — not an experiment we're considering.

The Environment

This team sits inside one of the largest health organizations in the country.

What that provides: Clinical and claims data at a scale that would take a startup years to build through partnerships. The ability to deploy to millions of people from day one. Deep domain colleagues — clinicians, actuaries, operations and compliance experts — whose knowledge makes your products better.