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. 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.

Applied AI Science Co-Op

As an Applied AI Science Co-Op, you will research and implement methods to improve representation learning, embedding quality, and personalized ranking systems, while also developing customer segmentation and behavior models that surface meaningful differences in research patterns. You will contribute to user skill modeling by estimating and leveling a customer’s genealogy expertise, enabling adaptive guidance and experiences that evolve as users grow.

You will collaborate closely with applied scientists, engineers, and product partners to translate research ideas into scalable, real-world production systems. These efforts are foundational to delivering meaningful, personalized family connections and extending Ancestry’s leadership in AI-powered discovery and customer understanding.

This is a part-time, work-study-based opportunity for students in active master's or PhD programs in 2026.

• Use data, embedding models, and personalization techniques to create meaningful, personalized family history experiences for customers
• Develop and evaluate models for customer segmentation, behavior understanding, and user skill progression in genealogy to inform adaptive product experiences
• Collaborate with applied scientists and software engineers to design, build, and deploy scalable machine learning solutions for discovery, recommendation, and customer insights
• Participate in technical discussions and knowledge sharing, contributing to a culture of strong machine learning, generative AI, and applied personalization practices

• Pursuing an advanced degree (MS or PhD; PhD preferred) in Computer Science, or a related field
• Demonstrated experience in applied research, including implementing and adapting published machine learning models or methodologies to solve real-world problems; prior publications in top-tier venues such as NeurIPS, ICML, ICLR, CVPR, ACL, KDD, or similar conferences are a plus
• Proficient in Python, SQL, and AWS and hands-on experience with applied machine learning techniques and hugging face; familiarity with embedding models, RAG, and representation learning is a plus
• Proficient in deep neural networks and modern ML frameworks such as PyTorch or Tensor Flow/Keras
• Exposure to large language models or generative AI applications, including prompt engineering, retrieval-augmented generation, or agent-based workflows

Ancestry is an Equal Opportunity Employer that makes employment decisions without regard to race, color, religious creed, national origin,…