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

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Commercial & Investment Bank

Associate Applied Researcher focused on building and productionizing end-to-end agentic AI systems for workflow automation in a quantitative trading and research environment. The role involves driving applied research, integrating emerging techniques, and ensuring robust, scalable, and observable production code.

What you'd actually do

  1. Build agentic systems end-to-end: design, prototype, and productionize multi-step LLM agents that retrieve context and generate accurate, well-structured responses
  2. Drive applied research by evaluating emerging techniques (tool use, planning, retrieval, evaluation frameworks, fine-tuning, prompt optimization) and integrating the best into production
  3. Own the full loop from problem framing and dataset construction through model/agent design, evaluation, deployment, and monitoring
  4. Improve quality systematically via evals, error analysis, and feedback loops that convert subjective issues into measurable fixes
  5. Partner cross-functionally with sales, quant research, trading, product, and engineering to deeply understand RFQ/client workflows and ship adopted solutions

Skills

Required

  • Python
  • owning production code
  • building with LLMs (agent frameworks, tool use, RAG, prompt engineering, evals)
  • understanding of modern GenAI capabilities, failure modes, and practical mitigation strategies
  • delivering ML/AI systems that moved a real business or user metric
  • explain technical tradeoffs to non-technical stakeholders
  • write clearly

Nice to have

  • Equity Derivatives and Pricing
  • evaluation frameworks
  • LLM observability
  • fine-tuning open-weight models
  • scaling an agentic prototype into a production system
  • designing and operating monitoring/QA processes for LLM outputs

What the JD emphasized

  • productionize multi-step LLM agents
  • integrating the best into production
  • ship adopted solutions
  • production-grade code and systems
  • bias to action
  • strong ownership mindset

Other signals

  • building agentic systems end-to-end
  • productionize multi-step LLM agents
  • integrating the best into production
  • ship adopted solutions
  • build and maintain production-grade code and systems