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

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Category:
Associates

Job Schedule:

Full time

Posted Date: T16:54:04+00:00

Job Shift:

Base Pay/Salary:
New York,NY $-$

Posting description

At JPMorgan

Chase, 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 product ionize 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)