Job description

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.ai (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 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 - Design and implement novel machine learning models and methods for computational pathology. - Translate machine learning and statistics papers into production-ready code. - Build robust model evaluation frameworks and monitor model performance. - 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. QUALIFICATIONS - PhD degree in machine learning, computer vision or statistics. - 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 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 and computational pathology. Experience in self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference or model interpretability is a bonus.

Requirements

Must-haves

Design and implement novel machine learning models and methods for computational pathology.
Translate machine learning and statistics papers into production-ready code.
Build robust model evaluation frameworks and monitor model performance.
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 machine learning, computer vision or statistics.
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 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.

Nice-to-haves

Experience in deep learning and computational pathology. Experience in self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference or model interpretability is a bonus.

Application prep

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Keywords to mirror

aimachine learningpython

Platform: ashby
Effort: quick

Benefits

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