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

Overview

In this role you will bridge quantitative research, low-latency engineering, and ML infrastructure to create production-ready platforms for AI-driven systematic trading. You will turn research concepts into robust, fast systems that manage market data, models, and execution with determinism and resilience. You’ll collaborate with researchers and traders to translate requirements into scalable software used in live markets. This is a chance to shape next-gen electronic trading infrastructure at a global leader in financial services.

Responsibilities

• Design high-performance components for market data, feature computation, backtesting, simulation, model serving, execution, and monitoring
• Develop low-latency C++ services and APIs integrating quantitative models with real-time data and order-management systems
• Build scalable data and research pipelines supporting historical data, reproducible experiments, and rapid strategy iteration
• Optimize critical paths for throughput, tail latency, memory efficiency, resilience, and determinism with profiling-guided decisions
• Productionize ML models, including training workflows, versioning, real-time inference, deployment automation, observability, and rollback controls
• Partner with researchers and traders to translate strategy requirements into robust software and support live systems

Key requirements

• Relevant professional experience in software engineering, quantitative development, low-latency systems, or ML infrastructure
• Strong modern C++ skills in data structures, concurrency, memory management, performance profiling, and production debugging
• Proficiency in Python and experience building software for quantitative researchers or data-intensive apps
• Understanding of distributed systems, testing, software design, reliability, and end-to-end production services
• Evidence of owning performance-critical systems from design to incident resolution

• collaboration
• problem-solving
• communication
• electronic trading architecture familiarity
• Linux performance engineering
• ML/data tooling such as PyTorch, JAX, CUDA, GPU clusters, Ray, Kafka, Kubernetes, Spark

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