What are the responsibilities and job description for the Quantitative Trading & Research - Applied Researcher – Agentic AI Systems - Associate position at JPMorgan Chase?
Posting description
At JPMorganChase, we’re building the next generation of AI-powered workflow automation. This is a hands-on role for someone who thrives on ambiguity, ships quickly, and is energized by hard technical challenges.
Job Summary
As an Associate Applied Researcher in the Quantitative Trading & Research (QTR) Team, you’ll sit at the intersection of applied research and production engineering turning frontier GenAI capabilities into reliable, high-leverage agentic systems that transform how we respond to inbound client requests.
Job responsibilities
- Build agentic systems end-to-end: design, prototype, and productionize multi-step LLM agents that retrieve context and generate accurate, well-structured responses
- Drive applied research by evaluating emerging techniques (tool use, planning, retrieval, evaluation frameworks, fine-tuning, prompt optimization) and integrating the best into production
- Own the full loop from problem framing and dataset construction through model/agent design, evaluation, deployment, and monitoring
- Improve quality systematically via evals, error analysis, and feedback loops that convert subjective issues into measurable fixes
- Partner cross-functionally with sales, quant research, trading, product, and engineering to deeply understand RFQ/client workflows and ship adopted solutions
- Build and maintain production-grade code and systems that are observable, robust, and scalable
- Contribute to technical direction and standards for agent design, evaluation, and safe deployment
Required qualifications, capabilities, and skills
- Strong coding skills (Python preferred) and comfort owning production code
- Advanced degree in Computer Science, Data Science, Machine Learning, or related field.
- Hands-on experience building with LLMs (agent frameworks, tool use, RAG, prompt engineering, evals)
- Strong understanding of modern GenAI capabilities, failure modes, and practical mitigation strategies
- Demonstrated applied research track record delivering ML/AI systems that moved a real business or user metric
- Ability to explain technical tradeoffs to non-technical stakeholders and write clearly
- Bias to action; comfortable working in ambiguity with rapidly evolving requirements
- Strong ownership mindset with a focus on improving what’s broken without waiting for permission
Preferred qualifications, capabilities, and skills
- Experience with Equity Derivatives and Pricing
- Familiarity with evaluation frameworks, LLM observability, or fine-tuning open-weight models
- Experience scaling an agentic prototype into a production system used by real users
- Experience designing and operating monitoring/QA processes for LLM outputs (quality, safety, reliability)