About the role
About Ataraxis AI Ataraxis is a clinical AI research lab working at the intersection of multi-modal AI and precision medicine. Our goal is to make disease predictable. To accomplish this, we develop new AI methods that predict patient outcomes and treatment response, and build clinical tools to assist physicians in selecting the most optimal treatments for their patients. Our AI research lab discovers and develops methods to recognize patterns and predict outcomes across complex, multi-modal clinical data. This spans our causality (Ataraxis™ Tau), foundation model (Falcon and Kestrel for digital pathology), and survival analysis research. Our first clinical products, such as Ataraxis™ Breast for breast cancer, already help patients get the most appropriate treatment across the best academic institutions and community clinics worldwide. At Ataraxis, you will have a unique opportunity to shape not only the future of our company, but also the future of healthcare. You will join an exceptional team at the forefront of clinical AI research and deployment. Our advisors include AI pioneers such as our founding advisor, Yann LeCun, and distinguished oncologists from top cancer research institutions, all united by the mission to redefine precision medicine. Ataraxis has raised over $24 million in funding, including a $20 million Series A led by top venture capital funds such as Thiel Capital/Founders Fund (OpenAI, SpaceX, Palantir), Obvious Ventures (AMI Labs, Inceptive, Radical Numerics, Recursion), and AIX Ventures (Hugging Face, Perplexity). We are an company with a flat organizational structure, where every team member is empowered to actively contribute. Leadership roles are earned by those who demonstrate initiative and consistently deliver exceptional results. Strong work ethic and the ability to prioritize ruthlessly are essential.
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.
