Member of Technical Staff, Research Engineer

Responsibilities

• Implement novel machine learning models 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 performance
• Develop pipelines for data preprocessing, integration, and quality assurance
• Optimize code for efficiency on GPU clusters, focusing on speed and scalability
• Deploy machine learning models to the cloud via optimized inference pipelines
• Maintain high standards of scientific documentation to ensure reproducibility
• Collaborate with a multidisciplinary team of engineers and scientists to disseminate results through research papers and abstracts

Requirements

• 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
• Proficiency in Python and PyTorch
• Knowledge of computer architecture, parallel training, and GPU optimization
• Hands-on experience with deep learning

Preferred Qualifications

• Experience in self-supervised learning, survival analysis, multi-modal learning, or causal inference
• Experience with computational pathology or domain adaptation
• Prior publications in A* conferences such as ICML, ICLR, NeurIPS, or CVPR
• Ability to communicate complex results to both technical and non-technical audiences

Benefits

• Competitive salary range of $120K – $210K plus equity
• Fortune 500-level medical, dental, and vision insurance plans
• 401(k) plan with a 4% employer match
• Opportunities for rapid career advancement within a flat organizational structure

About the Company

Ataraxis AI is a clinical AI research lab working at the intersection of multi-modal AI and precision medicine. Our mission is to make disease predictable by developing AI methods that predict patient outcomes and treatment response. Backed by over $24 million in funding from leading investors like Thiel Capital and Obvious Ventures, we build clinical tools that help physicians select optimal treatments. Our research spans causality, foundation models, and survival analysis, supported by an advisory team of AI pioneers and distinguished oncologists.