Quantitative Trading & Research - Quantitative Developer - Associate or Vice President

JPMorgan Chase & Co.; Greater London; On-site; GBP 140,000 - 230,000; Full time

Job summary

JPMorgan Chase & Co. in London is seeking an Associate or Vice President for the QTR AI Market Lab to build the research and production technology behind AI-driven systematic trading.

You will work at the boundary of quantitative research, low-latency engineering, and ML infrastructure, creating reliable platforms that shorten the path from raw market data to production strategies. The role focuses on turning ambiguous research requirements into fast, reproducible software while respecting

Qualifications

• Bachelor's/Master's/PhD in computer science, engineering, mathematics, or a related technical discipline.
• Relevant professional experience in software engineering, quantitative development, low-latency systems, or ML infrastructure.
• Strong modern C++ skills: data structures, concurrency, memory management, performance profiling, and production debugging.
• Proficiency in Python and experience building software for quantitative researchers or other data-intensive applications.
• Solid understanding of distributed systems, testing, software design, reliability, and operating production services end-to-end.
• Evidence of owning performance-critical systems from design, deployment, monitoring to incident resolution.

Responsibilities

• Design and build high-performance market-data, feature-computation, backtesting, simulation, model-serving, execution, and monitoring components for systematic trading.
• Develop reliable low-latency C++ services and APIs that integrate quantitative models with real-time market data, pricing, risk controls, and order-management systems.
• Build scalable data and research pipelines that support granular historical data, reproducible experiments, distributed computation, and rapid strategy iteration.
• Optimize critical paths for throughput, tail latency, memory efficiency, resilience, and deterministic behavior; use profiling and measurement to guide engineering decisions.
• Productionize machine-learning models, including training workflows, model versioning, real-time inference, deployment automation, observability, and rollback controls.
• Partner with researchers and traders to translate strategy requirements into robust software, improve research-to-production consistency, and support live systems.

Skills

Modern C++

Python

Distributed systems

ML infrastructure

Production debugging

Education

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

Tools

Kafka

Kubernetes

CUDA

PyTorch

JAX

Spark

Job description

Join a pioneering team at the forefront ofsystematic trading innovation.The Quantitative Trading & Research (QTR) group is responsible for systematic trading across FX, Rates, Commodities, Credit, Equity and a wide range of markets. Within QTR, AI Market Lab brings together quantitative research, modern artificial intelligence, market microstructure, and high-performance engineering to develop the next generation of electronic trading capabilities. Our work spans signal research, pricing, market making, execution, portfolio construction, risk management, and the production systems that support them.

Job Summary

As an Associate or Vice President for the QTR team, you will build the research and production technology behind AI-driven systematic trading. You will work at the boundary of quantitative research, low-latency engineering, and ML infrastructure, creating reliable platforms that shorten the path from raw market data and research prototypes to monitored, resilient production strategies.
This role is ideal for engineers who enjoy turning ambiguous research requirements into clean interfaces, fast systems, and reproducible workflows—without losing sight of trading realities like latency, determinism, and operational risk.

Job Responsibilities

• Design and build high-performance market-data, feature-computation, backtesting, simulation, model-serving, execution, and monitoring components for systematic trading.
• Develop reliable low-latency C++ services and APIs that integrate quantitative models with real-time market data, pricing, risk controls, and order-management systems.
• Build scalable data and research pipelines that support granular historical data, reproducible experiments, distributed computation, and rapid strategy iteration.
• Optimize critical paths for throughput, tail latency, memory efficiency, resilience, and deterministic behavior; use profiling and measurement to guide engineering decisions.
• Productionize machine-learning models, including training workflows, model versioning, real-time inference, deployment automation, observability, and rollback controls.
• Partner with researchers and traders to translate strategy requirements into robust software, improve research-to-production consistency, and support live systems.

Required Qualifications, Capabilities, and Skills

• Bachelor's, Master's, or PhD in computer science, engineering, mathematics, or a related technical discipline (or equivalent professional experience).
• Relevant professional experience in software engineering, quantitative development, low-latency systems, or ML infrastructure.
• Strong modern C++ skills: data structures, concurrency, memory management, performance profiling, and production debugging.
• Proficiency in Python and experience building software for quantitative researchers or other data-intensive applications.
• Solid understanding of distributed systems, testing, software design, reliability, and operating production services end-to-end.
• Evidence of owning performance-critical systems from design, deployment, monitoring to incident resolution.

Preferred Qualifications, Capabilities, and Skills

• Experience with electronic trading architecture: exchange connectivity, market-data normalization, order management, pre-trade risk, or execution systems.
• Knowledge of Linux performance engineering: kernel/network tuning, lock-free programming, hardware-aware optimization, or FPGA-adjacent systems.
• Experience with ML/data tooling such as PyTorch, JAX, CUDA, GPU clusters, Ray, Kafka, Kubernetes, Spark, or comparable technologies.
• Understanding of market microstructure, backtesting pitfalls, transaction costs, and the operational needs of live quantitative strategies.
• Experience in environments operating real-time systems (hedge fund, proprietary trading firm, market maker, exchange, or financial institution).