Applied AI Scientist – AI Fellowship Program
The AI fellowship rotates engineers from the central platform team onto investment desks. Fellows build production research automation and AI workflows for portfolio managers.
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.
- Deliver analytical tooling with the central AI team
- Build agent workflows and data pipelines for desk research
- Translate investor requests into evaluated applications
- Rotate across teams and improve reusable platform components
What they ask for
Required
- Three to five years delivering production LLM tools
- Python engineering and statistical learning
- Agent-application development
- User-facing requirements work
- LLM API integration
- Clear explanation of technical trade-offs
Preferred
- Buy-side research exposure
- Cloud workflows
- Education
- Quantitative or computing master study, or equivalent work experience; advanced study preferred.
- Experience
- 3–5 years scoping and deploying user-facing LLM applications.
- Languages and tools
- Markets
- multi-asset
How to prepare
- Build an evaluated research agent with source tracing
- Demonstrate a deployed LLM application and its failure handling
- Explain an investor workflow before and after automation
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 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.