Unlearn.AI

Unlearn.AI

San Francisco, CA
60 Total Employees
37 Local Employees
Year Founded: 2017
Jobs at Unlearn.AI

Search the 6 jobs at Unlearn.AI

Recently posted jobs

Artificial Intelligence • Machine Learning
The Cloud Services team at Unlearn maintains the core cloud infrastructure for AI technology development. This internship role involves building and maintaining cloud-based systems to support AI technology development. Requirements include pursuing a degree in Computer Science, experience with Linux and scripting languages, familiarity with networking and container technologies, and the ability to work in San Francisco.
Artificial Intelligence • Machine Learning
Work as an intern at Unlearn, a tech company advancing AI in medicine. Develop a standardized base for statistical computing for clinical trials using R and Python. Collaborate with Research Statisticians to enhance team efficiencies and work on statistical methodologies.
Artificial Intelligence • Machine Learning
We are looking for an experienced engineering manager to lead a team and deliver the software interface to Unlearn’s AI-powered Digital Twin technology. In this high-impact role, you will manage a team of experienced full stack software engineers and partner closely with our Product and Technology teams to lead initiatives that execute the delivery of software products to customers.
Artificial Intelligence • Machine Learning
Data Scientists working on our Clinical Data teams at Unlearn are responsible for building world class internal data products that enable fundamental advancements in machine learning.
Artificial Intelligence • Machine Learning
This internship involves working on the Data Tools team at Unlearn to build internal data products for driving clinical trial timelines towards zero. Responsibilities include building software tools to transform clinical data into machine learning-ready datasets.
Artificial Intelligence • Machine Learning
Internship working with the Machine Learning group to develop new ML models of disease progression and gain exposure to generative modeling of tabular time series data.