Quant jobs / JPMorgan

Quantitative Trading & Research – Applied Researcher, Agentic AI Systems

An applied AI role in JPMorgan's QTR team, building agentic LLM systems that handle inbound client requests. It is mainly engineering and applied ML, linked to the markets business.

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.

Design and ship multi-step LLM agents that retrieve context and write responses
Test new agent, retrieval and prompt techniques and adopt the best
Build datasets and evaluation tests to measure and improve quality
Work with sales, quant and trading on client request workflows
Monitor and maintain the agents in production

What they ask for

Required

Strong Python coding and production ownership
Advanced degree in CS, data science or ML
Hands-on LLM building (agents, tool use, RAG, evals)
Understanding of GenAI failure modes
Applied ML track record
Clear communication
Comfort with ambiguity

Preferred

Equity derivatives and pricing knowledge
LLM observability and fine-tuning
Scaling prototypes to production
LLM output monitoring

Education: Advanced degree in computer science, data science, machine learning or similar
Experience: Not stated; Associate level
Languages and tools: Python

How to prepare

Build a retrieval-augmented agent with tool use and a proper evaluation set
Learn LLM failure modes and how to measure them
Study how derivatives quotes and RFQ workflows work
Practise shipping a monitored, tested ML service

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 JPMorgan’s site before applying: requirements, location and pay can change after we check them.

View the posting on JPMorgan’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with JPMorgan and doesn’t handle applications.