This listing doesn't post a salary. Most similar roles pay
$126,800–$228,433.
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
1117 open roles
on FindRole.
Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
As a Quantitative Trading & Research - AI Scientist - Associate within the AI Market Lab, you will join a team focused on developing next-generation electronic trading capabilities across FX, Rates, Commodities, Credit, and Equity markets. You will conduct deep research to pre-train Transformer-based and time-series foundation models from scratch using large-scale market, order-book, and transaction datasets. Your daily work involves developing data representations, tokenization schemes, self-supervised objectives, and distributed training recipes while investigating scaling laws, regime robustness, and inference costs. You will fine-tune these models for alpha generation, pricing, and risk management tasks. The role requires expertise in PyTorch or JAX to build large-scale data pipelines and infrastructure components. Essential skills include experience with mixed precision, checkpointing, and evaluation protocols that connect pre-training metrics to economically meaningful outcomes like out-of-sample prediction and simulated trading.
Pre-train Transformer-based and time-series foundation models from scratch using large-scale market, order-book, and transaction 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.
Analyze scaling laws, regime robustness, data efficiency, and trade-offs between model quality, inference cost, and latency.
Design evaluation protocols that connect pre-training metrics to economically meaningful outcomes like out-of-sample prediction and simulated trading.
Build reusable training, checkpointing, evaluation, and model-serving components in collaboration 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 including Transformer, LLM, multimodal, or time-series models.
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/technical quality through successful large-model training, high-impact research, open-source systems, or production deployment.
Experience with fine-tuning/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 with live trading deployments (preferred).
C++
Python
PyTorch
JAX
CUDA
Kubernetes
Kafka
Spark
Ray
Distributed Systems
Low-latency Engineering
Linux Performance Engineering
order-management systems
market-data normalization
Backtesting
Profiling
Concurrency
Python
KDB
C++
Java
Machine Learning
Data Analytics
Statistical Modeling
Backtesting
Risk Management
Derivatives Pricing
Feature Engineering
Signal Research
Software Engineering
AI