AI-Driven Quant Developer for Low-Latency Trading

JPMorganChase; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

J.P. Morgan is seeking a Quantitative Trading & Research – Quantitative developer to join the AI Market Lab in London. You will work at the boundary of research, engineering, and ML infrastructure, building reliable platforms that convert market data and research into deployed, monitored trading strategies.

The role emphasizes turning ambiguous requirements into fast, reproducible systems while managing latency, determinism, and operational risk in production environments.

Qualifications

• Bachelor’s/Master’s/PhD in computer science, engineering, mathematics, or related technical discipline (or equivalent).
• Professional experience in software engineering, quantitative development, or ML infrastructure.
• Strong modern C++ skills (data structures, concurrency, memory management, profiling).
• Python proficiency for quantitative research software.
• Understanding of distributed systems, testing, reliability, and production services.

Responsibilities

• Design and build high-performance market-data, backtesting, and execution components for systematic trading.
• Develop low-latency C++ services integrating models with real-time data and order-management systems.
• Build scalable data and research pipelines for historical data, experiments, and rapid strategy iteration.
• Optimize throughput, memory efficiency, and determinism with profiling.
• Productionize ML models including training workflows, inference, and deployment automation.
• Collaborate with researchers and traders to translate strategy requirements into robust software.

Skills

C++

Python

Low-latency

Distributed systems

ML infrastructure

Performance profiling

Education

Bachelor’s/Master’s/PhD in CS or related

Tools

Linux

Git

MPI/CUDA

Job description

J.P. Morgan is seeking a Quantitative Trading & Research – Quantitative developer to join the AI Market Lab in London. You will work at the boundary of research, engineering, and ML infrastructure, building reliable platforms that convert market data and research into deployed, monitored trading strategies.

The role emphasizes turning ambiguous requirements into fast, reproducible systems while managing latency, determinism, and operational risk in production environments.