Applied AI Engineer (Summer Internship)
The Applied AI internship builds tools that help investment teams use textual information. Projects span research, storage, retrieval and generative-model applications.
What you’d work on
Our reading of the posting: what a new hire in this role would most likely spend their first months on.
- Investigate generative-AI approaches for an investment workflow
- Build text ingestion and retrieval components
- Train or adapt language and embedding models
- Evaluate results and explain limitations to investment users
What they ask for
Required
- Python and SQL
- Statistical learning
- Data processing
- LLM or embedding-model application experience
- ETL and code repositories
- Technical communication
Preferred
- AI research
- Cloud and vector databases
- Deep-learning frameworks
- Education
- Bachelor or master student in a quantitative field, graduating December 2027–May 2028.
- Experience
- Ten-week student internship.
- Languages and tools
- Markets
- multi-asset
How to prepare
- Build a small retrieval application with a documented evaluation set
- Compare embedding or model choices on a text task
- Show how an AI output can be traced to source material
What to study
- Probability, Statistics and Financial Data
- Quantitative Research, Data and Backtesting
- Financial Machine Learning
The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.
Open the roadmapApply
Read the full posting on Balyasny Asset Management’s site before applying: requirements, location and pay can change after we check them.
View the posting on Balyasny Asset Management’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Balyasny Asset Management and doesn’t handle applications.