DICOM / Imaging Engineer
6 days ago
About Unidoc
Unidoc is building a vendor-neutral validation cockpit for radiology AI: continuous metrics, drift/bias monitoring, version-to-version comparisons, and governance-ready reports, without sending PHI to the cloud. Our current MVP focuses on mammography. Hospitals run a small local tool; only derived metrics leave the network.
What you'll do
- Build a lightweight Local Runner (CLI/container) that runs inside a hospital's research network to:
- Parse DICOM SR/SC and normalize outputs to JSON/CSV
- Generate ephemeral ROI thumbnails/overlays to verify "did AI highlight the right area?" and auto-purge ≤48h
- Log run manifests, hashes, and purge events for audit
- Create vendor-agnostic mapping profiles for different AI outputs; maintain robust validators and error handling
- Integrate with Orthanc/DICOMweb (QIDO/WADO/STOW) for batch pulls/pushes when available
- Collaborate with Python metrics pipeline (comparator, calibration, drift/fairness) and the TS web team (dashboards/exports)
- Document secure file handling, purge behavior, and operational runbooks
Must-have
- 3–7 years hands-on with DICOM (pydicom, dcmdump, dcmtk), including DICOM SR/SC parsing
- Experience with Orthanc and DICOMweb (QIDO/WADO/STOW)
- Strong Python (or Go) for CLI tools; solid Linux, containers (Docker), scripting, and logging
- Pragmatic approach to privacy/security (no PHI off-box, purge disciplines, audit logs)
Nice-to-have
- Experience with breast CAD/AI outputs, lesion ROIs/heatmaps
- Basic stats (IoU, hit/miss scoring), lightweight image ops (PIL/OpenCV)
- Exposure to HL7/FHIR or interface engines (Mirth) is a plus
Why this role matters
You unblock hospital pilots by making it simple, safe, and reproducible to run AI validation locally—no agents, no viewer rebuilds, no PHI leaving.
How we'll evaluate
- 30-min intro call → 3–4 hr take-home → 60-min tech deep-dive → references
- We value shipping, documentation, and privacy discipline over buzzwords.
Take-home (3–4 hrs, time-boxed)
- Convert a provided DICOM SR sample to normalized JSON (fields: study_uid, model_name, version, score, ROI coords)
- Produce a PNG overlay or cropped ROI thumbnail from pixel data (we'll provide a companion DICOM)
- Write a purger that deletes generated images after N hours and logs a manifest (JSON)
- Submit code + a short README with run steps
Screening questions
- Describe one DICOM SR parsing tool or script you've built (what outputs, for what use?).
- Have you implemented any DICOMweb workflows (QIDO/WADO/STOW)? Briefly explain.
- Share a link or gist demonstrating a CLI or containerized imaging utility you created.
- What's your approach to handling PHI safely and proving purge occurred?
EEO & privacy
RadiNate is an equal-opportunity employer. We respect patient privacy and will ask you to demonstrate the same rigor in interviews and work.
Apply
Email: (subject: DICOM Engineer — Your Name) with resume + links (GitHub/portfolio)
Contact:
Job Types: Full-time, Part-time
Projected Total Compensation: ₹80, ₹130,000.00 per month
Expected hours: 32 – 56 per week
Work Location: Remote
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