Machine Learning Engineer - Summer Intern 2027 (AI)

Location: In-person work is required at the Cambridge headquarters or New York City office, United States.

Company

hirify.global is S&P Global’s AI innovation hub, developing machine learning, natural language processing, data discovery, and financial generative AI solutions.

What you will do

• Solve challenges in agentic design and LLM orchestration, including context engineering, data access patterns, memory management, and agent evaluation.
• Lead a project to prototype, build, and test machine learning models and pipeline components with guidance from senior engineers.
• Participate in the full ML lifecycle, from problem framing and model selection through pipeline development, evaluation, and deployment support.
• Collaborate with Machine Learning Engineers, Product Managers, Designers, and Full-Stack Engineers.
• Present complex methods and results to both technical and non-technical audiences.

Requirements

• Pursuing a bachelor’s degree or higher with relevant machine learning coursework or internship experience.
• Experience designing and iterating on agentic systems and evaluating agent performance.
• Knowledge of advanced machine learning methods and statistical modeling of real-world data.
• Expertise in Python and Python-based machine learning frameworks such as LangGraph, Pydantic AI, or PyTorch.
• Effective coding, documentation, and communication skills.
• Ability to work in person from the Cambridge headquarters or New York City office is required.

Nice to have

• Experience with information retrieval, semantic search, textual RAG systems, LLM tool utilization, or LLM code generation.
• Familiarity with tools including Transformers, Hugging Face, LightGBM, scikit-learn, XGBoost, Pandas, Apache Spark, AWS, Docker, Airflow, FastAPI, Streamlit, or Gradio.

Culture & Benefits

• Work alongside experienced engineers with active mentorship and continuous feedback.
• Collaborate in a communicative, cross-functional environment focused on engineering best practices.
• Attend technical and non-technical discussions and company-wide social events.
• Receive autonomy and support while developing scalable, robust, and accurate machine learning solutions.