Machine Learning Engineer Intern

S&P Global

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Cambridge, MANew York, NY
Employment: Intern
Posted: 2 days ago
Freshness: Confirmed live yesterday
Closes: Sep 9, 2027
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $220k

$168k most similar roles pay here $289k

This listing doesn't post a salary. Most similar roles pay $184,868–$254,750.

Based on 240 similar postings.

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About S&P Global

S&P Global delivers Essential Intelligence® that shapes decision making. We provide the world’s leading organizations with the right data, connected technologies and expertise they need to move ahead.

S&P Global currently has 46 open roles on FindRole.

Listed pay typically runs $142,000–$200,000 across 37 roles with salary data.

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At a glance

TL;DR · Machine Learning Engineer Intern

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Machine Learning Engineer - Summer Intern 2027 As a Machine Learning Engineer - Summer Intern 2027, you will join the Kensho team to develop a cutting-edge GenAI platform, LLM-powered applications, and fundamental AI toolkit solutions like Kensho Extract. You will work under the guidance of senior engineers to prototype, build, and test ML models and pipeline components while navigating the full model lifecycle from problem framing to deployment. Your daily work involves solving challenges in agentic design, LLM orchestration, context engineering, and memory management. The role requires proficiency in Python and frameworks such as LangGraph, Pydantic AI, PyTorch, HuggingFace, and Scikit-Learn. You will utilize tools like Apache Spark, AWS, Docker, and FastAPI to address complex problems in speech recognition, entity linking, document extraction, and automated database linking within the business and financial generative AI space.

What does a Machine Learning Engineer earn in New York?

Median $216000 from 43 postings across 13 companies.

See salary data

Skills

Python LangGraph PyTorch HuggingFace Transformers RAG LLM Orchestration FastAPI Streamlit Gradio Apache Spark AWS S3 Pgvector OpenSearch Docker Airflow Jenkins vLLM DeepSpeed LiteLLM Pandas Matplotlib Jupyter Weights & Biases Langfuse DVC LabelBox Sklearn XGBoost LightGBM

What you'll do

Solve challenges in agentic design and LLM orchestration including context engineering and memory management.
Prototype, build, and test machine learning models and pipeline components under senior guidance.
Manage the full ML model lifecycle from problem framing to selection and deployment support.
Develop and evaluate agent performance to ensure systems meet specific user needs.
Implement advanced machine learning methods using Python-based frameworks like PyTorch or LangGraph.
Communicate complex technical results and methods effectively to both technical and non-technical audiences.

What we're looking for

Pursuing a bachelor's degree or higher with relevant classwork or internships in Machine Learning.
Experience designing and iterating on agentic systems and evaluating agent performance.
Experience with advanced machine learning methods.
Statistical knowledge, intuition, and experience modeling real data.
Expertise in Python and Python-based ML frameworks such as LangGraph, Pydantic AI, or PyTorch.
Demonstrated effective coding, documentation, and communication habits.
Ability to communicate complex methods and results to a broad, non-technical audience.

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