Python Developer Medical Imaging (3-D Reconstruction – Measurement)

Amplifier AI is building a surgical planning
platform powered by our own imaging,
segmentation and 3-D measurement engine.
We are a small team of engineers, radiologists
and data scientists from Ukraine and across
Europe. You would join the team developing the
internal Python engine behind 3-D
reconstruction and measurement for CT-based
workflows.

Read this before you apply

We hire for reasoning about 3-D space, not
for a title. The code itself is ordinary clean
Python the hard part is everything the
geometry touches:
Voxel space vs. physical space, and every
transform between them
Origin, spacing and direction consistent
across scanners, series and vendors
Real hospital data: incomplete series, odd
orientations, inconsistent metadata
Geometric edge cases surfaces that self-
intersect, clips that miss, distances that are
"almost" right
Reproducibility: the same input must give the
same number, today and in six months
If you can explain why a model is offset,
mirrored or at the wrong scale not just that it
looks wrong you will do well here.
Because the output feeds real surgical planning,
correctness is not negotiable. A wrong
millimetre is a clinical problem, not a bug report.
This role is probably not for you if you want
fully predefined tickets, a fixed roadmap, or to
implement tasks without understanding the
domain. Hospitals and real clinical data change
priorities; we adjust as a team.

What We're Looking For

Python engineering (the core of this role)
– Clean, modular, strictly typed Python. We run
mypy –strict, ruff, pytest, pre-
commit and deep, technical code reviews.

– Confident with NumPy / SciPy and array-
heavy numerical code.

– Ability to own a feature end-to-end: from
understanding the clinical problem to
validating the result on real data.

3-D medical imaging fundamentals
-Understanding of origin, spacing and
direction, and the difference between voxel
and physical coordinates.

-Reading DICOM series correctly, validating
RAS orientation, resampling without silently
corrupting geometry.

-Comfort debugging complex geometric
problems: distance, intersection, clipping,
surface generation from labels.

Toolchain
-SimpleITK, VTK or PyTorch experience is a
strong plus. If you haven't used them, you
must be genuinely comfortable diving deep
and learning independently we mentor, but
initiative is expected.
-Docker, Git, GitHub Actions, structured
logging.

Ways of working
-Clear technical communication you can
explain your reasoning and defend a design.
-Ownership over micromanagement. Good
written and spoken English.

What You'll Do
– DICOM & SimpleITK: read series correctly,
validate RAS origins and directions, resample
safely, maintain voxel physical transforms.
– VTK & geometry: generate surfaces from
labels; implement distance, intersection and
clipping algorithms; export reliable STL
markers and geometry artifacts.
– Measurement & QA logic: build robust
procedural tools for radiologists and handle
the edge cases real hospital data produces.
– Pipeline reliability: performance tuning,
structured logging, and making sure
pipelines survive messy multi-scanner
datasets.

Example challenges
– Build a robust distance-measurement tool
between anatomical segmentations, with
exportable nearest-point markers.
– Extract centerlines and anatomical landmarks
from noisy CT scans.
– Enforce consistent spacing / origin / direction
across multi-scanner datasets.

If these sound exciting rather than
overwhelming, you'll likely enjoy this role.

Nice to Have
– VTK surface / volume rendering experience.
– ML segmentation metrics and tooling (Dice,
Hausdorff, nnU-Net, PyTorch).
– GPU acceleration and large-volume
performance work.
– Background in radiology, biomedical
engineering or clinical imaging.

Our Stack
Python 3.13+ · SimpleITK · VTK · NumPy / SciPy
· Pydantic · PyTorch · nnU-Net · pytest · mypy
(strict) · ruff · pre-commit · Docker · GitHub
Actions · Git LFS · Sentry

What We Offer
Competitive salary, optional stock options, and
a clear path toward senior-level ownership and
technical lead responsibilities. Fully remote,
small team, no corporate theater.

How to Apply
– CV or LinkedIn
– GitHub or portfolio we value real code
– A short note: why does working in an
evolving product environment excite you?