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$145,200–$228,325.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1226 open roles
on FindRole.
Listed pay typically runs
$156,750–$215,000
across 5 roles with salary data.
Quantitative Trading & Research - AI Scientist - Associate will join the AI Market Lab to develop next-generation electronic trading capabilities. This role involves pre-training Transformer-based and time-series foundation models from scratch using large-scale market, order-book, transaction, and cross-asset datasets. Key responsibilities include developing data representations, tokenization schemes, self-supervised objectives, and distributed training recipes. You will fine-tune models for alpha generation, pricing, market making, execution, and risk-management tasks while studying scaling laws, data efficiency, and regime robustness. The position requires proficiency in PyTorch or JAX to build large-scale data pipelines and distributed training systems. You must possess deep knowledge of optimization, parallelism, mixed precision, and checkpointing to solve complex problems in systematic trading across FX, Rates, Commodities, Credit, and Equity markets.
Pre-train Transformer-based and time-series foundation models from scratch using large-scale market and order-book datasets.
Develop data representations, tokenization schemes, self-supervised objectives, 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, data efficiency, and trade-offs between model quality, inference cost, and latency.
Design evaluation protocols connecting pre-training metrics to economically meaningful outcomes like out-of-sample prediction and simulated trading.
Build reusable training, checkpointing, evaluation, and model-serving components with ML infrastructure engineers.
What we're looking for
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, 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 (preferred).
Prior work on time-series foundation models, limit-order-book modeling, multimodal market data, or cross-asset transfer learning (preferred).
Experience in quantitative trading, HFT, electronic market making, or systematic investing, especially with models deployed to live trading (preferred).
Python
C++
Machine Learning
Deep Learning
Reinforcement Learning
High-Frequency Trading
Market Microstructure
Quantitative Research
Signal Research
order-book modeling
Financial Engineering
data-analysis
Live Trading Systems
C++
Python
ML Infrastructure
Low-Latency Engineering
Distributed Systems
PyTorch
JAX
CUDA
Kubernetes
Kafka
Spark
Ray
Linux Performance Engineering
order-management systems
market-data normalization
Backtesting
model-serving
GPU Clusters
Lock-free Programming
C++
Java
Quantitative Research
Backtesting
Data Analysis
Low-latency Systems
Automated Trading Systems
Statistics
Probability
Production Code
FICC
Credit Market-making
Fixed Income
CDS Indices