Ataraxis AI
Member of Technical Staff, Computational Pathology
New York HQ · Staff+
Sponsorship not specifiedDetected 19 days ago
PythonMachine LearningDeep LearningComputer VisionStatisticsResearchLeadership
About the role
- About Ataraxis AI Ataraxis is a clinical AI research lab working at the intersection of multi-modal AI and precision medicine.
- Our goal is to make disease predictable.
- This spans our causality (Ataraxis™ Tau), foundation model (Falcon and Kestrel for digital pathology), and survival analysis research.
Responsibilities
- Build robust model evaluation frameworks and monitor model performance.
- Collaborate with a multidisciplinary team of engineers and scientists.
Requirements
- Strong work ethic and the ability to prioritize ruthlessly are essential.
- PhD degree in machine learning, computer vision 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) or top journals.
- 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 computational pathology. Experience in self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference or model interpretability is a bonus.
Benefits
- Design and implement novel machine learning models and methods for computational pathology.
- Translate machine learning and statistics papers into production-ready code.
- Experience in deep learning and computational pathology.
- Experience in self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference or model interpretability is a bonus.
Apply directly at Ataraxis AI →Create a free account for alerts like thisView Ataraxis AI immigration profile
This listing is sourced directly from Ataraxis AI's careers page and normalized into a canonical job model.