JP Morgan Chase

Runs a consumer bank and an investment bank

Quantitative Trading & Research - Multiple Teams

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeNew York, NY, USA$150k - $200kIn-person

About the Job

Requirements

Strong coding skills, preferably in Python, and comfort owning production code.
An advanced degree in Computer Science, Data Science, Machine Learning, or a related field.
Hands-on experience building with large language models, including agent frameworks, tool use, retrieval-augmented generation, prompt engineering, and evaluations.
Strong understanding of modern generative AI capabilities, failure modes, and practical mitigation strategies.
A demonstrated applied research track record delivering machine learning or artificial intelligence systems that improved a real business or user metric.
Ability to explain technical tradeoffs to non-technical stakeholders and write clearly.
Comfort working in ambiguity with rapidly evolving requirements.
An ownership mindset focused on improving broken processes without waiting for permission.

Responsibilities

Build agentic systems end-to-end by designing, prototyping, and productionizing multi-step large language model agents that retrieve context and generate accurate, well-structured responses.
Drive applied research by evaluating emerging techniques, including tool use, planning, retrieval, evaluation frameworks, fine-tuning, and prompt optimization, and integrate the best techniques into production.
Own the full cycle from problem framing and dataset construction through model and agent design, evaluation, deployment, and monitoring.
Improve quality systematically through evaluations, error analysis, and feedback loops that turn subjective issues into measurable fixes.
Partner cross-functionally with sales, quantitative research, trading, product, and engineering to understand RFQ and client workflows and deliver 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.

Desired Qualifications

Experience with equity derivatives and pricing.
Familiarity with evaluation frameworks, large language model 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 and quality-assurance processes for large language model outputs, including quality, safety, and reliability.

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)

Base Pay/Salary: New York,NY $150,000.00-$200,000.00

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1799

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