Memorial Sloan Kettering Cancer Center (MSK) is looking to extract insights from complex datasets, particularly in medical imaging and computational pathology, by applying advanced statistical methods, deep learning, and high-performance computing.
Requirements
- 3+ years applying deep learning to real-world problems, ideally in medical imaging or healthcare AI.
- Proficient in PyTorch and distributed/multinode training environments.
- Proven experience with model deployment in high-stakes environments.
- Strong programming expertise in Python, bash, and CUDA.
- Experience with cloud platforms (AWS, GCP), HPC environments (e.g., SLURM), and infrastructure as code (Terraform).
- Familiarity with containerization tools like Docker and Kubernetes.
- Knowledge of medical imaging, histopathology, and genomics preferred.
Responsibilities
- Design and deploy deep learning and transformer-based models tailored for pathology applications.
- Fine-tune large-scale AI and foundation models for clinical use cases.
- Optimize self-supervised and few-shot learning approaches for digital pathology.
- Translate research into production-ready AI tools used in healthcare environments.
- Partner closely with clinicians and computational pathologists to ensure clinical integration.
- Lead and contribute to high-impact publications in top AI and medical journals.
- Mentor junior researchers, engineers, and postdoctoral fellows on technical direction and execution.
Other
- PhD or equivalent experience in Computer Science, Machine Learning, Computational Biology, or related field.
- Ability to balance academic rigor with practical deployment in healthcare systems.
- Reporting to Physician Investigator, SKI
- Schedule: 9:00 AM – 5:00 PM EST, Monday - Friday
- Location: Hybrid; 99% remote with ability to come on site 1-2 times a year
