Agentic Personalization AI Research Co-Op

10 Ancestry.com Operations Inc.; United States; À distance; USD 28 000 - 41 000; Temps partiel

Résumé du poste

Ancestry seeks an exceptional Applied AI Science Co-Op to join our team this spring, focusing on agentic personalization and long-horizon discovery paths. You will work with applied scientists, ML engineers, and product partners to translate multi-agent concepts into scalable AI systems.

This part-time, work-study opportunity targets active graduate students pursuing advanced degrees (PhD preferred) in CS/AI, with hands-on ML research, Python/SQL/PyTorch proficiency, and experience with agent

Qualifications

• Pursuing an advanced degree (PhD strongly preferred) in Computer Science, AI, or a related field
• Experience with applied ML research, including implementing or adapting published ML methodologies; publications in NeurIPS, ICML, ICLR, ACL, KDD, RecSys, or similar venues are a plus
• Proficient in Python, SQL, and PyTorch, with experience building and evaluating ML systems
• Hands-on experience with agentic AI, including agent orchestration frameworks such as LangGraph, DSPy, AutoGen, or CrewAI, and concepts such as tool use, planning, memory, and multi-agent systems
• Experience with LLM inference, fine-tuning, RAG, or retrieval systems; familiarity with technologies such as vLLM, LoRA/PEFT, Qdrant, FAISS, HNSW, or Two-Tower models is a plus
• Familiarity with agentic evaluation, including LLM-as-a-Judge, RAG evaluation, or synthetic user simulation, is a plus

Responsabilités

• Research and develop agentic personalization systems that use customer behavior, genealogy data, and AI to create meaningful, adaptive family history experiences
• Explore AI memory and personalization architectures, including user profiles, preference representations, event history, and embeddings
• Develop hybrid agentic recommendation systems where LLM agents orchestrate retrieval, ranking, and personalization models
• Investigate long-term customer journeys and interactive experiences, moving beyond optimizing for the immediate next click
• Develop and evaluate agent-based and simulated-user frameworks for testing multi-step personalization and recommendation quality
• Collaborate with applied scientists and engineers to prototype and deploy scalable AI solutions for personalization, recommendation, discovery, and genealogy

Connaissances

Python

SQL

PyTorch

Agentic AI

LLM inference

Formation

PhD preferred

Outils

LangGraph

DSPy

AutoGen

CrewAI

vLLM

LoRA/PEFT

Qdrant

FAISS

HNSW

Two-Tower models

Description du poste

Ancestry seeks an exceptional Applied AI Science Co-Op to join our team this spring, focusing on agentic personalization and long-horizon discovery paths. You will work with applied scientists, ML engineers, and product partners to translate multi-agent concepts into scalable AI systems.

This part-time, work-study opportunity targets active graduate students pursuing advanced degrees (PhD preferred) in CS/AI, with hands-on ML research, Python/SQL/PyTorch proficiency, and experience with agent