Member of Technical Staff, Causality at Ataraxis AI (New York, NY)Member of Technical Staff, Causality

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.ai/research-causality (Ataraxis™ Tau), foundation model ataraxis.ai (Falcon and Kestrel for digital pathology), and survival analysis ataraxis.ai research.

Our first clinical products, such as Ataraxis™ Breast ataraxis.ai/ataraxis-breast-overview 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.

• Design and implement novel causal inference methods for treatment effect modeling.
• Translate machine learning papers into production-ready code.
• Build robust model evaluation frameworks.
• Disseminate the results by co-authoring research papers and abstracts.
• Collaborate with a multidisciplinary team of engineers and scientists.
• Co-mentor junior members of the team.

• PhD degree in causality, statistics or machine learning.
• Deep understanding of causal inference methods and concepts.
• Previous experience working with observational and randomized trial data.
• Passion for research, attention to detail and ability to drive tasks to completion. Strong preference will be given to candidates with papers in A* conferences (e.g. ICML, ICLR, NeurIPS, CVPR) or top-tier statistics and causality journals.
• Excellent understanding of core machine learning concepts.
• Excellent knowledge of the foundations of statistics, linear algebra, probability and machine learning.
• Excellent skills in Python and PyTorch.
• Experience in deep learning. Experience in survival analysis, multi-modal learning, domain adaptation, model interpretability and computational pathology is a bonus.
• Experience with medical data is a bonus.