Member of Technical Staff, Research Engineer

The role

You will implement novel machine learning models for self-supervised learning, survival analysis, multi-modal learning, causality and interpretability. You will translate research papers into production-ready code and deploy models to the cloud.

What you will do

• Implement novel machine learning models and methods
• Translate machine learning and statistics papers into production-ready code
• Build robust model evaluation frameworks and monitor model performance
• Develop pipelines for data preprocessing, integration, and quality assurance
• Maintain high standards of scientific documentation
• Optimize code to run efficiently on GPU clusters
• Deploy machine learning models to the cloud in optimized inference pipelines
• Develop and maintain regression and unit tests
• Disseminate the results by co-authoring research papers and abstracts
• Collaborate with a multidisciplinary team of engineers and scientists

Required qualifications

• BS/MS/PhD degree in computer science, machine learning or statistics
• Excellent understanding of core machine learning concepts
• Excellent knowledge of the foundations of statistics, linear algebra and probability
• Excellent skills in Python and PyTorch
• Proficiency in data visualization and communicating complex results to both technical and non-technical audiences
• Excellent understanding of computer architecture, parallel training of AI models, and GPU optimization
• Experience in deep learning
• Attention to detail and ability to drive tasks to completion
• Passion for research

Nice to have

• Experience in at least one of {self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference, model interpretability, computational pathology}
• Prior publications in A* conferences (e.g. ICML, ICLR, NeurIPS, CVPR)

About the company

Ataraxis is a clinical AI research lab working at the intersection of multi-modal AI and precision medicine. The company develops new AI methods to predict patient outcomes and treatment response.