AI Quant Researcher: Foundation Models for Markets

JPMorgan Chase & Co.; New York (NY); On-site; USD 180,000 - 260,000; Full time

Job summary

JPMorgan Chase & Co. is seeking an AI/ML quantitative researcher to lead pre-training of large foundation models from scratch using Transformer and time-series data. The role spans research, model development, and system-building for scalable, robust trading applications.

The position emphasizes deep mathematical and ML theory with practical constraints, focusing on scaling laws, data efficiency, and robustness to deliver measurable improvements across markets and regimes.

Qualifications

• Advanced degree (Master’s, PhD, or equivalent) in a quantitative field such as ML, CS, statistics, math, OR, or engineering.
• Experience pre-training a large model from scratch (Transformer/LLM/multimodal/time-series)—API usage alone is not sufficient.
• Experience building large-scale data pipelines and distributed training systems using PyTorch, JAX, or equivalent.
• Deep knowledge of large-model training and evaluation: optimization, parallelism, mixed precision, checkpointing, experiment design, ablations, benchmarking.
• Evidence of research/technical quality through successful large-model training, high-impact research, open-source systems, or production deployment.

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 trade-offs among model quality, inference cost, and latency.
• Design evaluation protocols connecting pre-training metrics to economically meaningful outcomes, including out-of-sample prediction, simulated trading, costs, capacity, and live markouts.
• Build reusable training, checkpointing, evaluation, and model-serving components with ML infrastructure engineers.

Skills

Machine learning

Computer science

Statistics

Mathematics

Operations research

Engineering

Education

Advanced degree (Master’s or PhD) in a relevant quantitative field

Tools

PyTorch

JAX

Job description

JPMorgan Chase & Co. is seeking an AI/ML quantitative researcher to lead pre-training of large foundation models from scratch using Transformer and time-series data. The role spans research, model development, and system-building for scalable, robust trading applications.

The position emphasizes deep mathematical and ML theory with practical constraints, focusing on scaling laws, data efficiency, and robustness to deliver measurable improvements across markets and regimes.