Member of Technical Staff, Research Engineer
Research Engineer implementing novel ML models for clinical AI, focusing on self-supervised learning, survival analysis, multi-modal data, causality, and interpretability to predict patient outcomes in precision medicine. Requires strong Python/PyTorch skills, deep learning experience, and statistics foundations; publications are a plus.
About the job
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.
- 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.
Skills
Python, PyTorch, Machine Learning, Deep Learning, Self-Supervised Learning, Survival Analysis, Multi-Modal Learning, Causal Inference, Model Interpretability, Gpu Optimization, Statistics
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