Quantitative Trading & Research - AI/ML Quantitative Researcher - Associate or Vice President

Reposted One Month Ago
Be an Early Applicant
London, Greater London, England, GBR
Hybrid
Expert/Leader
Financial Services
We’re one of the world’s biggest technology-driven companies
The Role
Lead research and development of Transformer-based and time-series foundation models for systematic trading. Responsibilities include pre-training models from scratch on large-scale financial datasets, designing representations and self-supervised objectives, developing distributed training systems, fine-tuning models for trading applications, studying scaling and regime robustness, and evaluating economic performance through simulations and live-trading metrics.
Summary Generated by Built In

Join a pioneering team at the forefront of systematic trading innovation. The Quantitative Trading & Research (QTR) group is responsible for systematic trading across FX, Rates, Commodities, Credit, Equity and a wide range of markets. Within QTR, AI Market Lab brings together quantitative research, modern artificial intelligence, market microstructure, and high-performance engineering to develop the next generation of electronic trading capabilities. Our work spans signal research, pricing, market making, execution, portfolio construction, risk management, and the production systems that support them. 

Job Summary

As a Quantitative Trading & Research – AI/ML Quantitative Researcher, you will lead research on building Transformer-based and time-series foundation models over large-scale market datasets, and develop the methods needed to make them robust, transferable, and measurable across instruments and regimes. We are seeking an AI/ML quantitative researcher with hands-on experience pre-training large foundation models from scratch.

This role is designed for someone who wants to do deep research with real constraints - where questions like scaling laws, data efficiency, and robustness are not academic footnotes, but the core of the agenda.

Job Responsibilities

  • Pre-train Transformer-based and time-series foundation models from scratch using large-scale market, order-book, transaction, and cross-asset datasets.
  • Develop data representations, tokenization schemes, self-supervised objectives, model architectures, and distributed training recipes for financial time series.
  • Fine-tune and post-train foundation models for alpha generation, pricing, market making, execution, and risk-management tasks.
  • Study scaling laws, transfer across instruments and asset classes, regime robustness, data efficiency, and the trade-offs among model quality, inference cost, and latency.
  • Design evaluation protocols that connect pre-training metrics to economically meaningful outcomes, including out-of-sample prediction, simulated trading, transaction costs, capacity, and live markouts.
  • Build reusable training, checkpointing, evaluation, and model-serving components with ML infrastructure engineers.

Required Qualifications, Capabilities, and Skills

  • Advanced degree (Master’s, PhD, or equivalent experience) in machine learning, computer science, statistics, mathematics, operations research, engineering, or a related quantitative field.
  • Demonstrated experience pre-training a large model from scratch (Transformer/LLM/multimodal/time-series). Experience limited to API usage or prompt engineering is not sufficient.
  • Experience building large-scale data pipelines and distributed training systems using PyTorch, JAX, or equivalent frameworks.
  • Deep knowledge of large-model training and evaluation: optimization, parallelism, mixed precision, checkpointing, experiment design, ablations, and benchmarking.
  • Evidence of research/technical quality through successful large-model training, high-impact research, open-source systems, or production deployment.

Preferred Qualifications, Capabilities, and Skills

  • Experience with fine-tuning/post-training for forecasting, ranking, decision-making, or structured prediction.
  • Prior work on time-series foundation models, limit-order-book modeling, multimodal market data, or cross-asset transfer learning.
  • Experience in quantitative trading, HFT, electronic market making, or systematic investing - especially with models deployed to live trading.
  • Publications at leading ML venues and/or substantial contributions to large-scale model-training systems.
About UsJ.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.
  
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
About the TeamJ.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world. 

Skills Required

  • Advanced degree in machine learning, computer science, statistics, mathematics, operations research, engineering, or a related quantitative field
  • Demonstrated experience pre-training a large model from scratch, such as a Transformer, LLM, multimodal, or time-series model
  • Experience building large-scale data pipelines and distributed training systems using PyTorch, JAX, or equivalent frameworks
  • Deep knowledge of large-model training and evaluation, including optimization, parallelism, mixed precision, checkpointing, experiment design, ablations, and benchmarking
  • Evidence of research or technical quality through successful large-model training, high-impact research, open-source systems, or production deployment
  • Experience with fine-tuning or post-training for forecasting, ranking, decision-making, or structured prediction
  • Experience with time-series foundation models, limit-order-book modeling, multimodal market data, or cross-asset transfer learning
  • Experience in quantitative trading, high-frequency trading, electronic market making, or systematic investing, especially with models deployed to live trading
  • Publications at leading machine learning venues or substantial contributions to large-scale model-training systems

JPMorganChase Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.

  • Healthcare Strength — Medical coverage and wellness resources, including mental-health support and wellness incentives, are described as comprehensive and accessible; feedback suggests income-based contributions help keep care affordable.
  • Retirement Support — A dollar-for-dollar 401(k) match up to 5% of eligible pay, plus additional employer contributions in some cases, is highlighted as a strong pillar of financial security. Feedback suggests this structure is a notable advantage of the total rewards package.
  • Parental & Family Support — Up to 16 weeks of paid parental leave for all parents, along with fertility, adoption, surrogacy, childcare, and family-support resources, signals a family-forward approach. Feedback suggests these programs are a standout feature of the package.

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The Company
HQ: New York, NY
289,097 Employees
Year Founded: 1799

What We Do

JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.

Why Work With Us

Our technologists work on a diverse range of solutions that include strategic technology initiatives, big data, mobile, electronic payments, machine learning, cybersecurity, enterprise cloud development, and other state-of-the-art technologies.

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