[Remote] Founding AI-Native Engineer

Note: The job is a remote job and is open to candidates in USA. Rockstar is recruiting for an AI-native operations platform for patient access, focused on enrolling patients in assistance programs and verifying coverage. They are seeking one or two senior engineers to build this platform, where each hire will play a significant role in architectural decisions and product ownership.

Responsibilities

• Designing and shipping the AI agents that run patient case lifecycle — application intake, document extraction, data validation, coverage verification, patient communication, missing-info follow-up — including the evaluation harness, confidence thresholds, and human-in-the-loop boundaries
• PHI-aware data architecture: de-identification pipeline that strips identifiers before inference, the tokenization layer that maps results back, and the audit trail that logs every model call
• Integrations with healthcare systems of record and dependent services – as well as the orchestration layer that coordinates across them
• The production bar: on-call, incident response, rollback strategy, and strict uptime SLAs
• Working directly with operations, IT, and clinical teams during discovery, UAT, and go-live — you'll be in the room, not behind a PM

Skills

• 5+ years shipping production software, with meaningful time owning backend systems that mattered when they broke
• Deep expertise in at least one modern backend language (Python, TypeScript, Kotlin, Go, or similar) and the judgment to know when the choice matters and when it doesn't
• Comfort across the full backend picture: relational databases, cloud infrastructure (AWS preferred), containerized services, API design, and the operational discipline that comes with running regulated software
• A real sense for UX — you care how the thing feels to use, not just whether it works. You've sat with users, sweated the details, and pushed back on designs that looked good but didn't hold up in the workflow
• You've built something real with LLMs — orchestration, retrieval, tool use, structured output, evals, handling model failure modes
• Track record of taking an ambiguous problem, shaping it into an architecture, and shipping it
• You treat model behavior as something to test, measure, and govern. You've thought about what 'evals' means for a system where being 95% right isn't good enough
• Bonus: healthcare or other regulated-industry experience; prior early-stage or founding-engineer time; Salesforce ecosystem; eligibility engines, document extraction, health compliance or case-management workflows
• Eastern Time preferred, +/- 3 hours

Company Overview