Data Science - AI Document Understanding, Co-op

Overview

We are seeking an exceptional and highly motivated AI Engineer / Data Science Co-op to join our AI Applied Science Content team. You’ll play a vital role in the design and implementation of AI Native agentic systems that extract and organize text and image information from billions of historical and genealogical records, enabling customers to discover, share, and connect with their family history.

The work will focus on building autonomous, multi-agent workflows capable of complex reasoning, tool use, analysis, and self-correction. You will also work closely with engineering teams to train, optimize, and deploy solutions that promote product development, customer success, and content creation across our Family History business. This is a part-time, work-study-based opportunity designed for active master's and PhD students continuing their education in the fall.

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.

We will foster a work environment that is 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.

Note:

All job offers are contingent on a background check screen that complies with applicable law. For candidates who live in San Francisco, CA, Ancestry will consider for employment qualified applicants with arrest and conviction records in accordance with the San Francisco Fair Chance Ordinance.

• Innovate with State-of-the-Art AI:
  Implement cutting-edge AI solutions for key Document Understanding tasks such as OCR/HTR, transcription, Named Entity Recognition (NER), Relation Extraction (RE), Coreference Resolution, Summarization, and Knowledge Graphs working with diverse genealogical and historical collections spanning newspapers, city directories, family history books, and vital records (i.e., birth, marriage, & death records).
• Analyze and Optimize Multi-Modal Models:
  Evaluate the performance of multi-modal models in zero-shot and few-shot learning scenarios for comprehensive document understanding.
• Architect Agentic Systems:
  Design and implement multi-agent workflows using frameworks like Lang Chain, Lang Graph, CrewAI, or Auto Gen to automate complex multi-step reasoning tasks in historical document analysis.
• Evaluation & Observability:
  Establish "LLM-as-a-Judge" frameworks and use tools like Arize Phoenix, Deep Eval, or RAGAS to monitor for hallucination, drift, and bias.
• Collaborate on Cloud Deployment:
  Partner closely with ML Ops and Data Science Engineers to seamlessly deploy datasets, models, and pipelines in cloud environments.
• Communicate Insights Effectively:
  Clearly and confidently present your findings, deliverables, and proposed solutions to technical and non-technical audiences, including teams, stakeholders, and executives.

• Currently pursuing an advanced degree (Master's or PhD preferred) in Computer Science, Data Science, Statistics, Mathematics, Linguistics, Engineering or related quantitative field with a strong data focus.
• Specialization in AI & LLMs including familiarity with foundational models such as GPT, Gemini, Qwen, Llama, Claude, etc.
• Experience with inference optimization, vLLM, LoRA, QLoRA, quantization, etc.
• Familiar with embeddings, vector databases, transformer models, with software development experience.
• Strong proficiency in Python and relevant tools and libraries, including transformer models, multi-modal models, and general NLP (e.g., Hugging Face Transformers, agentic frameworks and workflows, Lang Chain, Lang Graph,…