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About the Role

Nucs AI is revolutionizing cancer care with AI and medical imaging technology. We are building AI-powered tools at the convergence of medical imaging, radioligand therapy, and artificial intelligence to enhance diagnostic precision and expand access to expert-level cancer care, improving patient outcomes worldwide.

Responsibilities

• 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

• 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 (GCP or equivalent).
• 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.
• 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.

Skills

• Data Science
• Data Engineering
• Medical AI
• Medical Imaging
• Statistical Analysis
• Machine Learning
• Deep Learning
• LLM
• Cloud Data Infrastructure
• GCP
• DICOM
• NIfTI
• Data Lineage
• Data Provenance
• Traceability
• Reproducibility
• Software Practices
• SHAP

Location

• Remote

Work Type

• Remote-first
• Flexible working

Experience Level

• 3+ years of relevant full-time experience

Benefits

• Meaningful equity
• Competitive equity package

About the Company

• 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.
• Tackling the growing demand for accurate, timely cancer diagnostics in the face of rising scan volumes and limited radiologist capacity.
• Building AI-powered tools at the convergence of medical imaging, radioligand therapy, and artificial intelligence.
• Starting with prostate cancer and expanding across oncology.
• Partnering with world-leading medical institutions and pharmaceutical companies across the US, Europe, and Australia.
• Venture-backed, early-stage.
• Building a team that blends deep clinical expertise with engineering intensity.
• Mission to enhance diagnostic precision and expand access to expert-level cancer care, improving patient outcomes worldwide.
• Work alongside leading oncologists, nuclear medicine physicians, and AI researchers globally.
• Radioligand therapy and AI-driven oncology are multi-billion dollar growth markets.

Equal Opportunity

• Nucs AI is an equal opportunity employer.
• We celebrate diversity and are committed to creating an inclusive environment for all team members.
• All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.