Note: The job is a remote job and is open to candidates in USA. Adentris builds AI-powered compliance and revenue integrity infrastructure for behavioral health providers using HIPAA-compliant, data-sovereign systems. The Founding Engineer will own the platform’s technical direction end to end, including clinical data integration, AI evaluation, architecture, and product development. The role also involves working directly with customers and founders while establishing the engineering standards for the growing team.
Responsibilities
- First 30 days: ship to production. Take over one of our five live modules (Documentation QA is the busiest), get its evaluation harness under your control, and ship an improvement a customer notices
- By day 60: own the EHR data layer. We integrate with the behavioral-health-native EMRs (Kipu, Alleva, BestNotes and others), and the ingestion and normalization architecture across them is yours to set
- By day 90: you've made at least one architecture decision the company will live with for years, defined how we evaluate model output before a clinician sees it, and set the bar the next engineering hires will be measured against
- Architecture across the full stack: data model, ingestion, AI pipelines, application layer, infrastructure
- The clinical data layer: ingesting and normalizing messy EHR data: FHIR, HL7, and the many formats that pretend to be them
- AI systems clinicians actually trust: evaluation, ground truth, error analysis. Calling a model is easy. Proving the output is right is the job
- Product judgment from data: go into real client data, find quality gaps nobody has flagged, turn them into shipped features
- The engineering bar: as the team grows, you define how we hire, review, test, and ship
Skills
- 5+ years shipping production software, with real ownership of systems that outlived your involvement
- Deep TypeScript/Node and Python; advanced React and Next.js
- Data engineering at scale: schema design, query optimization, pipelines over large volumes of semi-structured records
- Production LLM systems, not demos. You've built the evaluation harness that kept them honest
- Communication that carries weight: equally clear with a clinician, a founder, and an engineer
- Full-time, remote (US), with team meetups in San Francisco
- Founding or early engineer at a startup that reached real scale
- Regulated data experience (healthcare, finance, legal); you've been through a SOC 2 or HIPAA audit rather than only read about them
- Hands-on with EHR data and clinical formats; embeddings and retrieval in production
- Self-hosted or private model inference experience (Azure ML, vLLM, or similar)
Benefits
- Full-time, remote (US), with team meetups in San Francisco
- Founding-level equity
- Direct line to the founders; short path from idea to production
Company Overview