About Nucs AI
Nucs AI is revolutionizing cancer care through cutting-edge AI and medical imaging technology. Founded in 2024 by a multidisciplinary team of oncologists, AI researchers, and healthcare innovators, we’re tackling one of medicine’s most pressing challenges: the growing demand for accurate, timely cancer diagnostics in the face of rising scan volumes and limited radiologist capacity.
We sit at the intersection of diagnostics and treatment planning - building AI-powered tools at the convergence of medical imaging, radioligand therapy, and artificial intelligence. Starting with prostate cancer and expanding across oncology, we partner with world-leading medical institutions and pharmaceutical companies across the US, Europe, and Australia to bring precision oncology into everyday clinical practice.
Our mission is to enhance diagnostic precision and expand access to expert-level cancer care, improving patient outcomes worldwide. We’re venture-backed, early-stage, and building a team that blends deep clinical expertise with engineering intensity - moving with the rigor the medical field demands and the speed the problem deserves.
What you'll do
Build auditable data pipelines for large clinical, diagnostic, and medical imaging datasets in the cloud.
Own statistical analysis across clinical studies, diagnostic performance, model evaluation, and data quality.
Apply classical machine learning and deep learning methods to data analysis problems.
Use LLM-based methods to extract structured data from clinical documents and reports.
Build dashboards and data observability tooling.
Investigate and document missing, inconsistent, invalid, and implausible data.
Maintain strong data lineage, provenance, traceability, and reproducibility across data workflows.
Produce analyses and supporting evidence that hold up in regulatory submissions and audits.
Requirements
What we're looking for
3+ years of relevant full-time experience in data science, data engineering, medical AI, medical imaging, or a closely related field.
Strong statistical depth applied to messy clinical or diagnostic data.
A track record of building and maintaining data pipelines at scale.
Comfort working with medical imaging formats including DICOM and NIfTI.
Practical experience with classical machine learning and explainable ML.
Working fluency with deep learning and modern LLM tooling.
Experience with cloud data infrastructure. We use GCP, but equivalent platforms are fine.
A strong working understanding of data lineage, provenance, traceability, and reproducibility.
Unusual attention to detail around data completeness, validity, consistency, and edge cases.
Flexibility at adapting to new technologies
Awareness of good software practices
Strongly preferred
Experience with imaging-based clinical trials, preferably in nuclear medicine.
Experience supporting FDA-cleared medical devices or EU MDR compliance.
Experience producing statistical analyses or technical evidence used in a regulatory submission.
Experience with DICOM de-identification and anonymization workflows.
Experience with model interpretability techniques such as SHAP.