Member of Technical Staff, Causality
Develops novel causal inference and treatment-effect modeling methods for clinical AI, translating research into production-ready code and evaluation frameworks. Requires a PhD in causality, statistics, or machine learning, strong Python and PyTorch skills, and experience with observational and randomized trial data.
About the job
Responsibilities
- Design and implement novel causal inference methods for treatment effect modeling.
- Translate machine learning papers into production-ready code.
- Build robust model evaluation frameworks.
- Disseminate results by co-authoring research papers and abstracts.
- Collaborate with a multidisciplinary team of engineers and scientists.
- Co-mentor junior team members.
Requirements
- PhD in causality, statistics, or machine learning.
- Deep understanding of causal inference methods and concepts.
- Experience working with observational and randomized trial data.
- Passion for research, attention to detail, and ability to drive tasks to completion.
- Strong preference for papers in A* conferences such as ICML, ICLR, NeurIPS, or CVPR, or in top-tier statistics and causality journals.
- Excellent understanding of core machine learning concepts.
- Strong foundations in statistics, linear algebra, probability, and machine learning.
- Excellent skills in Python and PyTorch.
- Experience with deep learning.
Nice-to-Haves
- Survival analysis.
- Multimodal learning.
- Domain adaptation.
- Model interpretability.
- Computational pathology.
- Medical data experience.
Skills
Causal Inference, Machine Learning, Python, PyTorch, Deep Learning, Statistics, Linear Algebra, Probability, Survival Analysis, Multimodal Learning, Domain Adaptation, Model Interpretability, Computational Pathology, Medical Data
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