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 is seeking an exceptional and highly motivated Data Science Co-Op to join our Content AI team, a dynamic group at the forefront of Document Understanding. You’ll play a vital role in developing innovative AI models 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. As a Co-Op on the Content AI team, you will build, train and fine-tune models that process historical documents to detect meaningful, personalized insights within historical documents that connect people to their ancestors. You will also work closely with engineering teams to train, optimize, and deploy models that promote product development, customer success, and content creation across our Family History business. What you will do: Innovate with State-of-the-Art AI: Implement and experiment with cutting-edge transformer and generative AI solutions for key Document Understanding tasks, including OCR, handwriting recognition, 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 (birth, marriage, death). 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. Collaborate on Cloud Deployment: Partner closely with ML Ops and Data Science Engineers to seamlessly deploy datasets, truth sets, models, and pipelines for training and inference 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. Who You Are: 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 generative AI & LLMs, embeddings, LoRA, QLoRA, vector databases, transformer models, Natural Language Processing (NLP), with software development expertise including data structures, distributed model training, and inference optimizations. Exhibit strong proficiency in Python and relevant tools and libraries, including those for transformer models, multi-modal models, and general NLP (e.g., Hugging Face Transformers, agentic frameworks and workflows, LangChain, LangGraph, NLTK). Familiarity with cloud platforms and related AI/ML services such as Google Gemini API, Vertex AI, AWS EC2, S3, SageMaker, Model Registry, and Bedrock is a plus. Additional Information: Ancestry is an Equal Opportunity Employer that makes employment decisions without regard to race, color, religious creed, national origin, ancestry, sex, pregnancy, sexual orientation, gender, gender identity, gender expression, age, mental or physical disability, medical condition, military or veteran status, citizenship, marital status, genetic information, or any other characteristic protected by applicable law. In addition, Ancestry will provide reasonable accommodations for qualified individuals with disabilities. All job offers are contingent on a background check screen that complies with applicable law. For candidates who live in San Francisco, CA, pursuant to the San Francisco Fair Chance Ordinance, Ancestry will consider for employment qualified applicants with arrest and conviction records. Ancestry is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at Ancestry via-email, the Internet or in any form and/or method without a valid written search agreement in place for this position will be deemed the sole property of Ancestry. No fee will be paid in the event the candidate is hired by Ancestry as a result of the referral or through other means. Let’s write the next chapter of history together. Our employees are fueled by our inquisitive nature and united by our shared commitment to help people discover, craft, and connect their family history. Whether you’re a natural problem solver, a strategic builder, an inspired creative, or something in between—we invite you to share your story and join us in making history.

Data Science - AI Document Understanding, Co-op.

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