Applied AI Science Co-op - Embedding models and Personalization

About Ancestry

When you join Ancestry, you join a human-centered company where every person’s story is important. Ancestry®, the global leader in family history, connects everyone with their past so they can discover, preserve, and share their unique family stories. With our unparalleled collection of more than 65 billion records, over 3.5 million subscribers, and over 27 million people in our growing DNA network, customers can discover their family story and gain a new level of understanding about their lives. Over the past 40 years, we’ve built trusted relationships with millions of people who have chosen us as the platform for discovering, preserving, and sharing the most important information about themselves and their families.

We are committed to our location flexible work approach, allowing you to choose to work in the nearest office, from your home, or a hybrid of both (subject to location restrictions and roles that are required to be in the office- see the full list of eligible US locations HERE). We will continue to hire and promote beyond the boundaries of our office locations, to enable broadened possibilities for employee diversity.

Together, we work every day to foster a work environment that's inclusive as well as diverse, and where our people can be themselves. Every idea and perspective is valued so that our products and services reflect the global and diverse clients we serve.

Ancestry encourages applications from minorities, women, the disabled, protected veterans and all other qualified applicants. Passionate about dedicating your work to enriching people’s lives? Join the curious. Ancestry seeks an exceptional, passionate, and highly motivated Applied AI Science Co-Op to join our team. Our team builds and advances the AI solutions behind Ancestry's content discovery, personalization, and information retrieval experiences. As an Applied AI Science Co-Op, you will research and implement methods to improve representation learning, embedding