Clinical Imaging Platform Engineer

a2z Radiology AI • Boston, MA

9h • $140,000 - $180,000 • Hybrid

About The Position

We build clinical AI that reads alongside radiologists. Our abdomen-pelvis CT triage device is FDA-cleared, and it's the first commercial system to simultaneously triage seven urgent conditions on abdomen-pelvis CT in the U.S. We're backed by Khosla Ventures. This role owns two of the things that decide how good our models can get: the quality of the labels going in, and whether a radiologist can see and trust what the model gives back. Radiologist time is the most expensive input we have. When a reader has to click four times to do something that should take one, we lose annotation throughput, and less throughput means weaker models, which eventually means a finding a patient's scan should have caught. So the interface a radiologist works in genuinely drives model quality, and this is a product engineering job as much as an infrastructure one. The harder half is knowing whether the labels are any good in the first place. Our annotation pipeline is built to measure itself: cases are claimed without race conditions, annotators move through defined phases, some batches are seeded with known ground truth, others are handed to more than one reader on purpose, and we score agreement with per-lesion Dice even when two readers worked from reconstructions that don't share a geometry. Getting that measurement right is most of the work. Today one engineer holds this whole surface while also carrying several others, and that's the gap we're hiring to close.

Requirements

• Experience building software that people used for hours a day and improving it by observing user behavior.
• Instinct to use metrics when assessing annotation quality.
• Comfort with coordinate systems to troubleshoot issues like masks being misaligned.
• Ability to fail safely by default when handling patient data.
• Capacity to disagree with radiologists on software while deferring to them on medical matters.

Nice To Haves

• Strong React and TypeScript with proven performance work.
• Backend API and async experience.
• Medical imaging experience (DICOM, Cornerstone3D, OHIF, PACS).
• Annotation tooling experience (from either side).
• AWS and Terraform experience.
• Segmentation or computer vision experience.
• Experience with inter-rater agreement and measurement design.
• Regulated software experience (ISO 13485, IEC 62304, HIPAA).
• Codec or streaming work such as HTJ2K.

Responsibilities

• Own the viewer, built on Cornerstone3D and VTK.js, including a unified volume-rendering path that falls back to stack rendering, multiplanar reformats generated on demand in any plane and clearly labeled as reformats, and progressive loading that builds a low-resolution volume from the first 10% or so of each HTJ2K codestream so the reader sees an image right away. This also includes correctness work related to radiological left/right, rulers under gantry tilt, MONOCHROME1 inversion, and signed-pixel codec mismatches.
• Own the annotation system, including 3D mask storage, AI-assisted click-to-segment across all three planes, classical tools like region growing, FWHM thresholding, multi-seed refinement, and the evaluation pipeline around them: case-pool ledgers, per-annotator phase state machines, ground-truth and peer-overlap batches, agreement scorecards, and cross-series resampling through NIfTI affines. The system must clearly indicate when a case cannot be scored, rather than recording it as a zero.
• Develop annotation schemas, including per-lesion-category schemas with gating by view, phase, and slice, and conditionally required fields. Collaborate with radiologists on these clinical calls.
• Develop model output that a radiologist can actually read, including a versioned sparse-RLE mask contract keyed by SOP Instance UID, and the geometry that maps a model's 512-square grid through image position, direction cosines, and pixel spacing into world-space contours that land on the right anatomy.
• Manage the platform underneath, which uses FastAPI on AWS, with per-study authorization enforced on every read, cohort isolation between customers, and access-trail auditing that meets HIPAA §164.312(b) for study-data reads. Focus on debugging DICOM geometry issues like coordinate systems, orientation, and affines.

Benefits

• Discretionary bonus (approximately 10%)
• Equity may be offered to top candidates (US, Boston hybrid)
• Cash-weighted compensation set by local market (international, remote)
• No visa needed, no immigration strings (international, remote)
• Cash-only offers (international, remote)
• Same repositories, data, and review authority as anyone on the team (international, remote)
• Written and asynchronous reviews (international, remote)

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed