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Based on 240 similar postings.
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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
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Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
Quantitative Trading & Research - Market Microstructure & High-Frequency - Associate joins the AI Market Lab team to develop next-generation electronic trading capabilities across various asset classes including FX, Rates, Commodities, Credit, and Equity. This research-forward role involves analyzing high-frequency Level 2, 3, and 4 order-book data to identify predictive structures and trading opportunities. The candidate will build alpha signals based on order flow and queue dynamics while designing market-making and risk-taking strategies that account for latency, fees, and market impact. Key responsibilities include developing simulation machinery, performing ablation studies, and moving strategies into production through collaboration with traders and engineers. Required skills include advanced degrees in quantitative disciplines, proficiency in Python, and desirable experience in C++. The role focuses on solving complex problems in market microstructure, pricing, execution, and portfolio construction within high-frequency trading environments.
Analyze high-frequency Level 2, 3, and 4 order book data to identify predictive structures and trading opportunities.
Develop alpha signals and trading features based on order flow, liquidity dynamics, and price formation.
Design and implement market-making and risk-taking strategies including pricing, order placement, and inventory control.
Build simulation methodologies that incorporate latency, fees, rebates, and market impact to ensure realistic backtesting.
Optimize strategy performance across signal generation, portfolio sizing, execution, and intraday risk management.
Translate research insights into production-ready designs that are aware of execution constraints and latency.
Conduct markout analysis and collaborate with partners to improve strategies based on live performance data.
What we're looking for
Advanced degree or equivalent practical experience in mathematics, statistics, physics, computer science, engineering, financial engineering, or a related quantitative discipline.
2+ years of full-time quantitative research experience in high-frequency/medium-frequency trading, electronic market making, or systematic execution.
Strong understanding of electronic market mechanics including order types, matching engines, queue priority, and market impact.
Evidence of contributing to strategies used in live markets with an understanding of the research-to-production workflow.
Strong programming and data-analysis skills in Python.
Proficiency in C++ or another high-performance language (preferred).
Demonstrated rigor in experimental design to identify overfitting, leakage, and regime-specific artifacts.
Experience in specialized areas like market making, alpha, execution research, or machine learning applied to order-book modeling (preferred).
Python
KDB
C++
Java
Machine Learning
Data Analytics
Statistical Modeling
Backtesting
Risk Management
Derivatives Pricing
Feature Engineering
Signal Research
Software Engineering
AI
Python
C++
Machine Learning
Stochastic Calculus
Probability Theory
Numerical Methods
Option Theory
LLM
Data Analysis
Risk Management
Volatility Surface Calibration
Hedging Optimization
Quantitative Research
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
Transformer
PyTorch
JAX
Machine Learning
Distributed Training
Quantitative Trading
Data Pipelines
Signal Research
Market Microstructure
Portfolio Construction
Risk Management
Benchmarking