Summary
What you’ll impact
The Lead Machine Learning Engineer – Medical Imaging will head a small ML research team at the organization, setting research direction, designing experiments, developing and training models, and mentoring engineers. The role involves hands‑on coding, translating research papers into implementations, building pipelines, and advancing successful work toward clinical validation and regulatory submission.
Responsibilities
What you'll do
- Lead ML research and mentor a small team while remaining actively involved in coding, training, and experimentation.
- Develop and evaluate methods involving 2D/3D medical imaging, reconstruction, image analysis, and generative imaging.
- Work with medical imaging datasets including X-ray, CT, DICOM, annotations, masks, and volumetric data.
- Translate promising research papers into working implementations and controlled experiments.
- Design validation methods that distinguish clinically supported information from visually plausible model output.
- Address data quality, registration error, hallucination, domain shift, uncertainty, and other model failure modes.
- Build reproducible preprocessing, training, inference, and evaluation pipelines.
- Help move successful research toward production, clinical validation, and regulatory submission.
Requirements
What you’ll bring
- 6+ years of applied ML/computer-vision experience.
- Advanced Python and PyTorch skills.
- Strong hands-on experience developing and training deep-learning models.
- Experience with medical imaging or closely related scientific imaging.
- Experience with CT, X-ray, CBCT, MRI, PET/SPECT, ultrasound, or other volumetric imaging.
- Strong understanding of 2D/3D computer vision, experimental design, dataset partitioning, validation, and error analysis.
- Demonstrated ability to take an open-ended research problem from literature → hypothesis → implementation → experimentation → failure analysis → improvement.
- Experience leading or mentoring ML engineers/researchers.