2027 AI Initiatives PhD Internship

StepStone Group•La Jolla, CA

4d•Hybrid

About The Position

StepStone's AI Initiatives team focuses on harnessing AI and machine learning to transform private markets investment research. The team spans the entire AI project lifecycle—from identifying high-impact use cases to deploying production-ready solutions—integrating cutting-edge technologies like LLMs, NLP, and advanced quantitative methods to drive strategic outcomes across portfolio analysis, investment performance, and risk assessment. StepStone is seeking highly analytical and technically skilled interns to join the AI Initiatives team. This role is best suited for candidates who combine strong Python and quantitative skills with hands-on experience in LLM application development. Interns will work on production-oriented projects involving LLM pipeline design, structured data extraction, and quantitative modeling to support investment research. We are looking for team players with a collaborative mindset and a strong work ethic—candidates who take ownership of their work but remain open to feedback, eager to learn from others, and committed to collective success. A low-ego, problem-solving approach is essential for success in this role.

Requirements

Pursuing a Ph.D. in Mathematics, Physics, Econometrics, Statistics, Engineering, or a related quantitative field.
Ability to work in-person in our La Jolla during the 2027 summer intern program and work part time remotely during the 2028 Fall semester.
Strong Python coding skills with experience in data pipelines and quantitative modeling; SQL proficiency required, with demonstrated ability to work with large, messy real-world datasets.
Experience or coursework in AI/ML (LLMs, NLP, data retrieval, vector embeddings) is highly preferred.
Strong problem-solving skills with a detail-oriented, hands-on approach to work.
Team-oriented and adaptable—able to balance independent work with collaborative projects.
Open to feedback and continuous improvement, with a strong intellectual curiosity.
Willingness to contribute at all levels—whether tackling complex modeling or assisting with data prep, no task is too small.
Candidates must be at least 18 years old to apply.

Nice To Haves

Familiarity with cloud data platforms (e.g. Snowflake, Azure), vector databases, or workflow orchestration tools (e.g. Airflow) is a plus.

Responsibilities

Design and iterate on LLM-powered pipelines for document processing tasks such as structured data extraction, RAG system design, prompt engineering, and LLM-output evaluation.
Build and optimize quantitative models and end-to-end data pipelines using Python, and SQL for large-scale financial data extraction and transformation.
Analyze large financial datasets and apply quantitative modeling to surface investment insights.
Document technical findings, model design decisions, and pipeline architecture; communicate results clearly to both technical teammates and non-technical investment stakeholders.