Quant jobs / Balyasny Asset Management

ML Engineer - Commodities Applied ML (Summer Internship - Master's)

This commodities internship develops analytical models and data products for investment teams. Potential projects use satellite, weather and other spatial information to evaluate market questions.

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.

Turn investment hypotheses into measurable research questions
Prepare structured and alternative datasets
Validate statistical and machine-learning models
Deploy and explain analytical outputs for investment teams

What they ask for

Required

Statistics and machine learning
Python and SQL
Reproducible research
Data-leakage awareness
Communicating uncertainty
Git and development practices

Education: The title specifies a master-level internship; the body does not publish additional degree or graduation criteria.
Experience: Ten-week internship.
Languages and tools: Python
SQL
Git
Markets: commodities

How to prepare

Build a weather or imagery-based prediction experiment
Use time-ordered validation to check leakage and overfitting
Present a data product with clear assumptions and error analysis

What to study

The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

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.