Quantitative Trading & Research - Applied Researcher – Agentic AI Systems - Associate

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)