Software Engineer, Latency-Critical Trading

About Millennium

Millennium is a global investment management firm built on a scalable, technology-driven platform. We run a diverse set of investment strategies and empower our teams to deliver exceptional outcomes by providing world-class tools, infrastructure, and data.

Team & Role Overview

The Latency-Critical Trading team is building a best-in-class systematic data platform to power the next generation of low-latency systematic strategies. The team includes low-latency Linux, network, datacenter, and C++ engineers focused on our end-to-end trading stack.

Key Responsibilities

• Monitor and assess the quality of live and historical market data; detect, inventory, and remediate data gaps.
• Maintain and document exchange session times, holiday schedules, timestamp rules, and protocol/microstructure changes.
• Analyze latency, data rates, bursts, and message flows to understand microstructure behavior and system performance.
• Clean, transform, and manage an inventory of large-scale datasets (including PCAP/PCAPNG) in a hybrid cloud/on-prem environment.
• Build and improve tools for market data capture.
• Work with vendors and brokers to assess and provision datasets.
• Build and improve tools for data analysis, visualization, and diagnostics on top of captured market and network data.
• Enhance and extend C++ analytics libraries and expose them within a Python environment for systematic research and alpha development.
• Collaborate closely with portfolio managers, quantitative researchers, and engineers to translate trading use cases into robust data and tooling solutions.

Qualifications

• Bachelor’s or Master's in Computer Science, Mathematics, Statistics, Engineering, or another quantitative field, or equivalent experience.
• 3+ years of experience in financial markets, electronic trading, or high-frequency / systematic environments preferred.
• Strong programming skills in Python, C++, and SQL; experience with R / MATLAB / SciPy stack / PyTorch or similar tools for data analysis.
• Solid understanding of modern statistical testing methods and comfort working with large, noisy, real-world datasets.
• Experience with Linux, large-scale data processing, and preferably network data (PCAP, timestamping, PTP) and low-latency systems.
• Strong problem-solving skills, attention to detail, and effective communication with both technical and non-technical stakeholders.