2027 AI Initiatives PhD Internship

We are global private markets specialists delivering tailored investment solutions, advisory services, and impactful, data driven insights to the world’s investors. Leveraging the power of our platform and our peerless intelligence across sectors, strategies, and geographies, we help identify the advantages and the answers our clients need to succeed.

Step Stone'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.

Step Stone 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.

Interns will design, build, and iterate on LLM‑powered solutions—including structured data extraction pipelines, RAG systems, and AI‑assisted investment research tools. You’ll work hands‑on with large financial datasets, applying quantitative modeling and statistical analysis to support investment performance research across Step Stone’s private markets portfolio.

• 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.

• 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, witha strong intellectual curiosity.
• Familiarity with cloud data platforms (e.g. Snowflake, Azure), vector databases, or workflow orchestration tools (e.g. Airflow) is a plus.
• Willingness to contribute at all levels—whether tackling complex modeling orassistingwith data prep, no task is too small.

At Step Stone, we foster a supportive and inclusive team culture where collaboration and learning are the core of what we do. Our collegial atmosphere encourages teamwork, mentorship, and professional development. This summer internship is a great opportunity to gain practical experience and develop critical skills that will help you launch a successful career in private markets.

Salary: $30 / hour

The salary is an estimate of pay for this position. Actual pay may vary depending on job-related factors that can include location,…