BS/MS/PhD in computer science, machine learning or statistics.
Strong understanding of core machine learning concepts.
Solid foundations in statistics, linear algebra, and probability.
Proficient in Python and PyTorch.
Skilled in data visualization and effective communication of complex results.
Knowledge of computer architecture and GPU optimization for AI model training.
Experience in deep learning, with knowledge of self-supervised learning, survival analysis, or related areas as a plus.
Detail-oriented with a strong task completion drive.
Passionate about research with prior publications in top conferences as a plus.
Responsibilities
Implement innovative machine learning models for various complex learning methods.
Translate academic machine learning research into practical, production-ready code.
Establish robust evaluation frameworks to monitor and assess model performance.
Create comprehensive data preprocessing and quality assurance pipelines.
Ensure high-quality scientific documentation for reproducibility in reports and publications.
Optimize code for efficient execution on GPU clusters, focusing on performance and scalability.
Deploy machine learning models using optimized cloud inference pipelines.
Develop and maintain thorough regression and unit tests for code integrity.
Co-author research papers and abstracts to share findings and results.
Collaborate effectively with a diverse team of engineers and scientists.
Benefits
Flexible working hours and remote work options.
Professional development and training opportunities.
Access to cutting-edge technology and tools.
Collaborative and supportive team culture.
Involvement in innovative research projects across multiple domains.
Full Job Description
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.
