Quantitative Trading & Research - AI Scientist, Quantitative Research - Associate/ Vice President

J.P. Morgan; Worcester (MA); On-site; USD 150,000 - 230,000; Full time

Job summary

J.P. Morgan seeks an AI/ML quantitative researcher to lead pre-training of large foundation models from scratch, focusing on Transformer-based and time-series models over expansive market data. You will shape representations, objectives, and architectures for robust and transferable models across instruments.

The role emphasizes real-world constraints, scalable training, and collaboration with ML infra engineers to deploy models for alpha generation, pricing, and risk tasks.

Qualifications

• Advanced degree in a quantitative field.
• Proven experience pre-training large models from scratch.
• Experience building large-scale data pipelines and distributed training systems.

Responsibilities

• Pre-train Transformer-based and time-series models on market data.
• Develop data representations and training objectives for financial time series.
• Fine-tune models for alpha generation, pricing, and risk tasks.
• Study scaling laws and data efficiency across instruments.
• Design evaluation protocols linking pre-training to trading outcomes.
• Collaborate with ML infra engineers on tooling and deployment.

Skills

Transformer pre-training

Time-series modeling

PyTorch

JAX

Distributed training

Large-model training

Education

Master’s or PhD in ML/CS

Tools

PyTorch

JAX

Experiment tooling

Large-scale data pipelines

Job description

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.
• 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.

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. 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. We are seeking an AI/ML quantitative researcher with hands-on experience pre-training large foundation models from scratch