Postdoctoral Fellow, AI Native Agentic Document Analysis

About the role

Ancestry is a human-centered company dedicated to connecting everyone with their past. We are the global leader in family history, offering over 65 billion records, 3.5 million subscribers, and a DNA network of over 27 million people. Our commitment to location flexibility allows employees to work in offices, from home, or a combination of both, depending on the role and location.

Responsibilities

• 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.
• Architect agentic systems: Design and implement multi-agent workflows using frameworks like LangChain, LangGraph, CrewAI, AutoGen, AgentCore, Strands, Google ADK, A2A, etc. to automate complex multi-step reasoning tasks in historical document analysis and information extraction.
• Analyze and optimize multi-modal models: Evaluate the performance of multi-modal AI & LLMs such as GPT, Gemini, Claude, Llama, and Qwen for zero-shot and few-shot scenarios in comprehensive document understanding.
• Natural Language Processing (NLP): NER, Relation Extraction, Coreference Resolution, Entity Resolution, Knowledge Graphs (Neo4j), spaCy, NLTK, BERT.
• Computer Vision (CV): Apply expertise using models like YOLO, Nougat, DONUT, OpenCV, etc. to perform layout analysis, identifying text blocks, headers, tables, and deeply nested lists.
• Evaluation & observability: Establish ensemble models and "LLM-as-a-Judge" frameworks, and use tools like Arize Phoenix, DeepEval, or RAGAS to monitor or hallucination, drift, and bias.
• Development productivity: Familiarity with "AI coding" workflows and usage of AI coding assistants such as Amazon Q, Cursor, Claude Code, and Kiro to accelerate development cycles.
• Collaborate on cloud deployment: Partner with ML Ops to deploy datasets, models, and pipelines in cloud environments like AWS (S3, SageMaker, Bedrock, ECS, EKS) and GCP (Vertex AI, Gemini API).

Requirements

A Ph.D. (recent graduate or near completion) in Computer Science, Data Science, Statistics, Linguistics, Engineering, or a related quantitative field with a strong research focus. A strong record of academic publications in NLP, CV, or Agentic AI is preferred. Specialization in AI & LLMs, including familiarity with foundational models such as GPT, Gemini, Qwen, Llama, Claude, etc. Research in inference efficiency and optimization, potentially using vLLM, LoRA, QLoRA, and quantization approaches. Familiarity with embeddings, vector databases, and transformer models, with software development experience. Strong proficiency in Python and relevant tools and libraries, including transformer models and multi-modal models. Familiarity with cloud platforms and related AI/ML services such as Google CloudPlatform, GCP, Gemini API, Vertex AI, AWS EC2, S3, SageMaker, Model Registry, and Bedrock are a plus. Ability to clearly present complex technical solutions to both technical and non-technical stakeholders.

Qualifications

Ph.D. (recent graduate or near completion) in Computer Science, Data Science, Statistics, Linguistics, Engineering, or a related quantitative field with a strong research focus. A strong record of academic publications in NLP, CV, or Agentic AI is preferred. Specialization in AI & LLMs, including familiarity with foundational models such as GPT, Gemini, Qwen, Llama, Claude, etc. Research in inference efficiency and optimization, potentially using vLLM, LoRA, QLoRA, and quantization approaches. Familiarity with embeddings, vector databases, and transformer models, with software development experience. Strong proficiency in Python and relevant tools and libraries, including transformer models and multi-modal models. Familiarity with cloud platforms and related AI/ML services such as Google CloudPlatform, GCP, Gemini API, Vertex AI, AWS EC2, S3, SageMaker, Model Registry, and Bedrock are a plus. Ability to clearly present complex technical solutions to both technical and non-technical stakeholders.

Skills

Ph.D. (recent graduate or near completion) in Computer Science, Data Science, Statistics, Linguistics, Engineering, or a related quantitative field with a strong research focus. A strong record of academic publications in NLP, CV, or Agentic AI is preferred. Specialization in AI & LLMs, including familiarity with foundational models such as GPT, Gemini, Qwen, Llama, Claude, etc. Research in inference efficiency and optimization, potentially using vLLM, LoRA, QLoRA, and quantization approaches. Familiarity with embeddings, vector databases, and transformer models, with software development experience. Strong proficiency in Python and relevant tools and libraries, including transformer models and multi-modal models. Familiarity with cloud platforms and related AI/ML services such as Google CloudPlatform, GCP, Gemini API, Vertex AI, AWS EC2, S3, SageMaker, Model Registry, and Bedrock are a plus. Ability to clearly present complex technical solutions to both technical and non-technical stakeholders.

Benefits

Location flexibility, including the option to work in the nearest office, from 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).

Pay

Competitive compensation package based on experience and qualifications.

Schedule

Flexible work schedule, including the option to work in the nearest office, from 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).