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

JPMorgan Chase & Co.; City of Rochester (NY); Presencial; USD 140.000 - 210.000; Jornada completa

Descripción de la vacante

JPMorgan Chase & Co. seeks an Associate Applied Researcher to advance GenAI-powered workflow automation within the Quantitative Trading & Research (QTR) team.

You will create agentic systems that translate RFQ/client workflows into reliable, scalable production solutions. Focus areas include prototyping multi-step LLM agents, evaluating tool-use and retrieval techniques, and owning the end-to-end lifecycle from data framing to monitoring.

Formación

• Strong coding skills (Python preferred) and comfort owning production code.
• Hands-on experience building with LLMs (agent frameworks, tool use, RAG, prompt engineering, evals).
• Advanced degree in Computer Science, Data Science, Machine Learning, or related field.
• 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.

Responsabilidades

• 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.

Conocimientos

Python

LLMs

Agent frameworks

Prompt engineering

Production code

System design

Educación

Advanced degree

Descripción del empleo

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)