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.
We are seeking an AI/ML quantitative researcher with hands-on experience pre-training large foundation models from scratch. 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.
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
- Advanced degree (Master’s, PhD, or equivalent experience) in machine learning, computer science, statistics, mathematics, operations research, engineering, or a related quantitative field.
- At least 2 years of relevant experience
- 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
- 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.
Skills Required
- Advanced degree in machine learning, computer science, statistics, mathematics, operations research, engineering, or a related quantitative field
- At least 2 years of relevant experience
- 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, HFT, electronic market making, or systematic investing, especially with live trading deployments
- 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.
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Healthcare Strength — Medical, dental, vision, and mental-health coverage are broad, with wellness incentives, on-site or virtual care, and an EAP offering coaching and counseling. Plan materials emphasize accessible options, including multiple medical choices and tools to manage costs.
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Parental & Family Support — Paid parental leave extends up to 16 weeks for all parents, supplemented by paid Critical Caregiver Leave. Family resources include backup childcare via Bright Horizons, lactation support and milk-shipping, family-building assistance, and even a free five-month SNOO rental for newborns.
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Retirement Support — Retirement programs include a 401(k) with an annual company match and automatic pay credits for most employees, with a legacy pension available to earlier hires. An Employee Stock Purchase Plan at a 5% discount further supports long-term savings.
JPMorganChase Insights
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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