Staff Applied AI Engineer

Job description

Doximity is transforming the healthcare industry. Join our mission to help every physician be more productive and provide better care for their patients. As medicine's largest network in the United States, there's an elevated level of responsibility in everything we do. We don't take that responsibility lightly and are committed to building diverse teams with an inclusive culture that can make a direct impact on the healthcare system. One of Doximity's core values is stretching ourselves. Even if you don't check off all the boxes below we encourage you to apply. Doximity is full of exceptional people who bring their own unique experiences to work everyday and make us all better for it! Visa sponsorship is not available for this role. About Us Here are some of the ways we bring value to doctors Here is an introduction to our tech stack We use UNIX command-line interface and standard programming tools (vim/emacs, git, etc.) and have over 350 private repositories in Github containing our applications, forks of gems, our own internal gems, and open-source projects We have worked as a distributed team for a long time; we're currently about 65% distributed Find out more information on the Doximity engineering blog Our company core values Our recruiting process Our product development cycle Our on-boarding & mentorship process Here's How You Will Make an Impact Leverage Doximity's extensive datasets to optimize, tune and evaluate AI products for medical professionals on our platform. Play a key role in creating both product and client-facing analytics. Inform data team strategy by working with the product leaders and managers. Actively participate in execution and some planning of organizational data team strategy. Collaborate with a team of product managers, analysts, and other developers to define and lead data projects from data ingestion to analysis to recommendations. About you At least 5 years of professional experience as a Data Scientist or other related roles working with complex, high-volume datasets. Advanced knowledge of statistical concepts — particularly exploratory data analysis, experimental design, and probability theory. Deep understanding of modern machine learning techniques, including deep learning architectures, reinforcement learning, and large language model (LLM) fine-tuning methods such as Supervised Fine-Tuning (SFT), Direct Preference O…