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Machine Learning Intern - Summer 2026

Kensho Technologies

Salary not specified
Sep 5, 2025
New York, NY, US
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Kensho is S&P Global’s hub for AI innovation and transformation, developing and deploying novel solutions for S&P Global and its customers worldwide. They are looking for ML Engineer interns to join the group of Machine Learning Engineers working on developing a cutting-edge GenAI platform, LLM-powered applications, and fundamental AI toolkit solutions such as Kensho Extract.

Requirements

  • Experience in designing and iterating on agentic systems, understanding user interactions, and evaluating agent performance to enhance user experiences.
  • Experience with advanced machine learning methods
  • Statistical knowledge, intuition, and experience modeling real data
  • Expertise in Python and Python-based ML frameworks (e.g.,LangGraph, Pydantic AI, PyTorch)
  • Demonstrated effective coding, documentation, and communication habits

Responsibilities

  • Apply advanced NLP techniques to extract insights from large proprietary unstructured and structured datasets
  • Design, build, and maintain scalable production-ready ML systems
  • Participate in the ML model lifecycle, from problem framing to training, deployment, and monitoring in production
  • Partner with our ML Operations team to deliver solutions for automating the ML model lifecycle, from technical design to implementation
  • Work in a cross-functional team of ML Engineers, Product Managers, Designers, Backend & Frontend Engineers who are passionate about delivering exceptional products

Other

  • Pursuing a bachelor's degree or higher with relevant classwork or internships in Machine Learning
  • Strong communication skills and the ability to effectively express even complicated methods and results to a broad, often non-technical, audience
  • We value in-person collaboration, therefore interns are required to work out of the Cambridge HQ or our New York City office!
  • All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.