About The Position

We are seeking passionate graduate-level ML/AI interns to join our Auto Physical Damage (APD) Intelligent Solutions team for Summer 2026 to work on cutting-edge ML/computer vision, generative AI systems, and claims management AI applications, and responsible AI initiatives. You will contribute to real-world projects involving computer vision, NLP, large language models, RAG, multi-agent systems, and AI safety frameworks while gaining hands-on experience with the latest AI innovations shaping our Property & Casualty industry. You will have the opportunity to work on several challenging Machine Learning, Generative as well as Responsible AI problems, including: Build models at scale using vast amounts of structured and unstructured heterogeneous data. Ensure high accuracy based on industry’s stringent requirements around precision or recall and with minimum Type I and Type II errors. Generate predictions for millions of rows of data with high response time, and low latency. Deal with high data diversity, very high dimensionality, and noisy data. Integrate Trustworthy Machine Learning, AI ethics, and governance. Learn about Causal Inference for AI Systems

Requirements

  • Pursuing a MS/PhD in Computer Science, AI/ML, Data Science, or related field
  • GPA of 3.5 or higher
  • Graduating Summer/Fall 2026 or Spring 2027
  • Ability to commit 40 hours per week for 12 weeks during the 2026 summer
  • Foundation in transformers, attention mechanisms, and neural net architectures
  • Experience with TensorFlow or PyTorch
  • Proficiency in Python and modern ML ops tools
  • Coursework in: Deep Learning, NLP, AI Ethics, or Trustworthy AI
  • Coding assisting tools like GitHub copilot, Cline, and knowledge around using scientific, distributed programming and scripting languages like Python, PySpark and/or Java preferred.
  • Strong foundation in mathematics, statistics, and machine learning algorithms
  • Knowledge of Human-AI Interaction Design

Nice To Haves

  • Published research or significant projects in generative AI applications
  • Contributions to open-source AI safety tools or bias detection frameworks
  • Familiarity with constitutional AI, RLHF, or interpretability methods
  • AI ethics certifications or responsible AI coursework

Responsibilities

  • Design and fine-tune large language models and multimodal AI systems
  • Implement RAG (Retrieval-Augmented Generation) pipelines and prompt engineering strategies
  • Work with open-source, open-weights and proprietary LLMs (Nova, Titan, Claude, & Gemini)
  • Integrate responsible AI & bias mitigation
  • Conduct fairness audits and bias testing across protected attributes (race, gender, age, license plate etc.)
  • Implement AI governance frameworks and risk assessment protocols
  • Develop evaluation metrics for model safety, hallucination detection, and alignment
  • Build autonomous AI agents using frameworks like AWS strands, AWS agent core or CrewAI
  • Design multi-agent orchestration for complex task automation
  • Create evaluation benchmarks for agent reliability and performance
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