Member of Technical Staff, Foundation Models
Design, implement, and evaluate novel self-supervised foundation models for clinical multi-modal data and precision medicine. Requires PhD in ML/statistics, strong publication record in top venues, and expertise in PyTorch/deep learning.
About the job
Responsibilities
- Design and implement novel self-supervised learning methods for training foundation models.
- Translate machine learning papers into production-ready code.
- Build robust model evaluation frameworks.
- Disseminate the results by co-authoring research papers and abstracts.
- Collaborate with a multidisciplinary team of engineers and scientists.
- Co-mentor junior members of the team.
Qualifications
- PhD degree in machine learning or statistics.
- Passion for research, attention to detail and ability to drive tasks to completion.
- Strong preference will be given to candidates with papers in A* conferences (e.g. ICML, ICLR, NeurIPS, CVPR).
- Excellent understanding of core machine learning concepts.
- Excellent knowledge of the foundations of statistics, linear algebra, probability and machine learning.
- Excellent skills in Python and PyTorch.
- Experience in deep learning and self-supervised learning.
- Experience in survival analysis, multi-modal learning, domain adaptation, causal inference, model interpretability and computational pathology is a bonus.
Skills
PyTorch, Python, Self-Supervised Learning, Deep Learning, Machine Learning, Statistics, Linear Algebra, Probability, Causal Inference, Multi-Modal Learning
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