Research Engineer (Systems/Optimization)

Ataraxis is an AI precision medicine company working at the intersection of multi-modal artificial intelligence and oncology. We develop and deploy AI-native tools to assist physicians in selecting the most optimal treatments for their patients.

Our mission is to change how cancer is treated and impact at least 50% of new cancer cases by 2030. Our first test for breast cancer patients was developed and evaluated in a validation study on over 8,000 patients from 15 international institutions. Ataraxis Breast was shown to improve the accuracy of gold standard tests by 30% and enable testing expansion to 100,000 new patients annually. At Ataraxis, you will have a unique opportunity to shape not only the future of our company, but also the future of healthcare.

At Ataraxis, you will join an exceptional team at the forefront of healthcare-focused AI research and deployment. As a frontier AI lab, we bring together leading AI researchers, medical doctors, and industry veterans. Our advisors include AI pioneers such as Yann LeCun and distinguished oncologists from top cancer research institutions, all united by the mission to redefine precision medicine.

Ataraxis has raised $24 million in funding, including a recent $20.4 million Series A round led by top venture capital funds such as AIX Ventures (Hugging Face, Perplexity), Thiel Capital, Founders Fund (OpenAI, SpaceX, Palantir, Oscar Health), and Obvious Ventures (Recursion). Our company was recently covered in an exclusive story in TechCrunch, "Not all cancer patients need chemo."

We are an AI-native 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.

• 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.

• 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.

• 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.

• 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 but is not critical.

• Attention to detail and ability to drive tasks to completion.

• Passion for research. Prior publications in A* conferences (e.g. ICML, ICLR, NeurIPS) are a plus but are not critical.