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

JPMorganChase; Greater London; On-site; GBP 120,000 - 170,000; Full time

Job summary

J.P. Morgan is seeking a Quantitative Trading & Research – AI/ML Quantitative Researcher to lead research on transformer-based and time-series foundation models built from large-scale market data.

You will push the boundaries of robust, transferable AI for electronic trading across FX, rates, and commodities. The role emphasizes hands-on pre-training of large models from scratch, with a focus on scaling laws, data efficiency, and robustness in live environments, working alongside ML infra

Qualifications

• Advanced degree (Master's, PhD, or equivalent experience) in ML/CS/math or related quantitative field.
• Experience pre-training a large model from scratch (Transformer/LLM/time-series).
• Experience building large-scale data pipelines and distributed training systems using PyTorch, JAX, or equivalents.
• Deep knowledge of large-model training, evaluation, optimization, and benchmarking.
• Evidence of research quality via large-model training, high-impact research, or production deployment.

Responsibilities

• Pre-train Transformer-based and time-series foundation models from scratch using large-scale market datasets.
• Develop data representations, tokenization schemes, self-supervised objectives, architectures, and distributed training recipes.
• Fine-tune and post-train foundation models for alpha generation, pricing, market making, execution, and risk management.
• Study scaling laws, transfer across instruments and regimes, robustness, data efficiency, and trade-offs among model quality, inference cost, and latency.
• Design evaluation protocols linking pre-training metrics to economically meaningful outcomes.
• Build reusable training, checkpointing, evaluation, and model-serving components with ML infrastructure engineers.

Skills

Advanced degree

Transformer training experience

Large-model training

Distributed training experience

Education

Master's degree or PhD

Tools

PyTorch

JAX

Distributed training systems

Job description

Job Description

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 Description

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 mark‑outs.
• 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 US

J.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 Team

J.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.