Applied AI Science Co-op — Part-Time, Remote/Hybrid

Ancestry; United States; Hybrid; USD 30,000 - 39,000; Part time

Benefits offered by this job

Location-flexible

Job summary

Ancestry seeks an exceptional Applied AI Science Co-Op to join our team. This part-time, work-study opportunity supports research in representation learning, personalization, and retrieval-augmented systems for family history discovery.

You will collaborate with applied scientists and engineers, implement ML methods, and help translate research into scalable production workflows. Enrolled in MS/PhD programs in 2026, this role emphasizes innovative thinking and rigorous experimental design.

Qualifications

• 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 ML models.
• Proficiency in Python, SQL, and AWS with hands-on ML techniques; familiarity with embedding models, RAG, and representation learning.

Responsibilities

• Use ML methods to improve representation learning, embedding quality, and personalized ranking.
• Develop models for customer segmentation and behavior understanding to inform adaptive experiences.
• Collaborate with scientists and engineers to design, build, and deploy scalable ML solutions.
• Participate in knowledge sharing and promote strong ML and AI practices.

Skills

Python

SQL

AWS

Hugging Face

Embedding models

RAG

Representation learning

PyTorch

TensorFlow

Generative AI

Prompt engineering

Education

MS in Computer Science

PhD in Computer Science

Tools

PyTorch

TensorFlow

Hugging Face

Job description

Ancestry seeks an exceptional Applied AI Science Co-Op to join our team. This part-time, work-study opportunity supports research in representation learning, personalization, and retrieval-augmented systems for family history discovery.

You will collaborate with applied scientists and engineers, implement ML methods, and help translate research into scalable production workflows. Enrolled in MS/PhD programs in 2026, this role emphasizes innovative thinking and rigorous experimental design.