Member of Technical Staff, Research Engineer

Responsibilities

• Implement novel machine learning models and methods for self-supervised learning, survival analysis, multi-modal learning, causality and interpretability.
• 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 to ensure reproducibility and clarity in both internal reports and external publications.
• Optimize code to run efficiently on GPU clusters, with emphasis on speed and scalability.
• Deploy machine learning models to the cloud in optimized inference pipelines.
• Develop and maintain regression and unit tests to ensure high-quality code.
• Disseminate the results by co-authoring research papers and abstracts.
• Collaborate with a multidisciplinary team of engineers and scientists.

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.
• Experience in at least one of {self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference, model interpretability, computational pathology} is a plus but is not critical.
• Attention to detail and ability to drive tasks to completion.
• Passion for research.
• Prior publications in A* conferences (e.g. ICML, ICLR, NeurIPS, CVPR) are a plus but are not critical.