Nucs AI
Machine Learning Scientist
Salary not disclosedRemote
- Engineering
- Remote
- Full time
- Yesterday
Newly posted
About the role
Nucs AI is seeking a Machine Learning Scientist to advance research capabilities in clinical oncology. This role involves developing novel methods for medical image analysis, treatment outcome prediction, and multi-modal clinical data integration to improve cancer diagnostics. You will collaborate with physicians and engineers to ensure research is scientifically rigorous and clinically applicable.
Responsibilities
- Develop novel ML/DL methods for medical image analysis, including segmentation, lesion detection, and outcome modeling
- Design and conduct experiments to evaluate new approaches against clinical baselines
- Publish findings in top-tier venues and represent the company at scientific conferences
- Collaborate with clinicians and medical physicists to ensure methods are grounded in clinical reality
- Produce validated research outputs with documented performance benchmarks
- Contribute to clinical validation studies and evidence generation for regulatory submissions
- Collaborate with external academic research partners on joint studies
Required skills
- Machine learning
- Computer vision
- Medical image analysis
- Biomedical engineering
- Deep learning
- CNNs
- Transformers
- U-Nets
- Python
- PyTorch
- Statistical foundations
- Experimental design
- Hypothesis testing
- Survival analysis
- Clinical biostatistics
Nice to have
- Nuclear medicine imaging
- Dosimetry
- PSMA-PET
- FDG-PET
- SPECT
- Treatment outcome prediction
- Longitudinal modeling
- Multi-modal data integration
- Clinical trial design
- Generative AI
Qualifications
- PhD in machine learning, computer vision, medical image analysis, biomedical engineering, or related field
Benefits
- Equity
- Remote-first work environment
- Flexible working
About the Company
Nucs AI is a venture-backed, early-stage company revolutionizing cancer care through AI and medical imaging technology. Founded in 2024, the team focuses on building AI-powered tools at the convergence of medical imaging, radioligand therapy, and artificial intelligence to enhance diagnostic precision.