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

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    "ExternalDescriptionStr": "<p><span style=\"font-size: 11pt;\"><strong>Posting description</strong></span></p><p><span style=\"font-size: 11pt;\">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.</span></p><p><span style=\"font-size: 11pt;\"><strong>Job Summary</strong></span></p><p><span style=\"font-size: 11pt;\">As an Associate Applied Researcher in the Quantitative Trading &amp; 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.</span></p><p>&nbsp;</p><p><span style=\"font-size: 11pt;\"><strong>Job responsibilities</strong></span></p><ul style=\"list-style-type: disc;\"><li><span style=\"font-size: 11pt;\">Build agentic systems end-to-end: design, prototype, and productionize multi-step LLM agents that retrieve context and generate accurate, well-structured responses</span></li><li><span style=\"font-size: 11pt;\">Drive applied research by evaluating emerging techniques (tool use, planning, retrieval, evaluation frameworks, fine-tuning, prompt optimization) and integrating the best into production</span></li><li><span style=\"font-size: 11pt;\">Own the full loop from problem framing and dataset construction through model/agent design, evaluation, deployment, and monitoring</span></li><li><span style=\"font-size: 11pt;\">Improve quality systematically via evals, error analysis, and feedback loops that convert subjective issues into measurable fixes</span></li><li><span style=\"font-size: 11pt;\">Partner cross-functionally with sales, quant research, trading, product, and engineering to deeply understand RFQ/client workflows and ship adopted solutions</span></li><li><span style=\"font-size: 11pt;\">Build and maintain production-grade code and systems that are observable, robust, and scalable</span></li><li><span style=\"font-size: 11pt;\">Contribute to technical direction and standards for agent design, evaluation, and safe deployment</span></li></ul><p>&nbsp;</p><p><span style=\"font-size: 11pt;\"><strong>Required qualifications, capabilities, and skills</strong></span></p><ul style=\"list-style-type: disc;\"><li><span style=\"font-size: 11pt;\">Strong coding skills (Python preferred) and comfort owning production code</span></li><li style=\"color: black;\"><span style=\"font-family: &quot;Open Sans&quot;;\"><span style=\"font-size: 10.5pt;\">Advanced degree in Computer Science, Data Science, Machine Learning, or related field.</span></span></li><li><span style=\"font-size: 11pt;\">Hands-on experience building with LLMs (agent frameworks, tool use, RAG, prompt engineering, evals)</span></li><li><span style=\"font-size: 11pt;\">Strong understanding of modern GenAI capabilities, failure modes, and practical mitigation strategies</span></li><li><span style=\"font-size: 11pt;\">Demonstrated applied research track record delivering ML/AI systems that moved a real business or user metric</span></li><li><span style=\"font-size: 11pt;\">Ability to explain technical tradeoffs to non-technical stakeholders and write clearly</span></li><li><span style=\"font-size: 11pt;\">Bias to action; comfortable working in ambiguity with rapidly evolving requirements</span></li><li><span style=\"font-size: 11pt;\">Strong ownership mindset with a focus on improving what’s broken without waiting for permission</span></li></ul><p>&nbsp;</p><p><span style=\"font-size: 11pt;\"><strong>Preferred qualifications, capabilities, and skills</strong></span></p><ul style=\"list-style-type: disc;\"><li><span style=\"font-size: 11pt;\">Experience with Equity Derivatives and Pricing</span></li><li><span style=\"font-size: 11pt;\">Familiarity with evaluation frameworks, LLM observability, or fine-tuning open-weight models</span></li><li><span style=\"font-size: 11pt;\">Experience scaling an agentic prototype into a production system used by real users</span></li><li><span style=\"font-size: 11pt;\">Experience designing and operating monitoring/QA processes for LLM outputs (quality, safety, reliability)</span></li></ul><p>&nbsp;</p>",
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