Senior Software Engineer - Quant Firm

This role is with one of Dex's trusted Partner companies. We work closely with their teams to truly understand their culture, goals, and what they're looking for, so we can match you with the right opportunity for your goals!

If you're interested, apply here, or head to the website, where you can sign up and speak to Dex. After your call, you'll be sent the full match details. We'll help manage your applications, and find other great matches to work you'll love.

The Opportunity

Dex is partners with some of the world's most prestigious quantitative trading firms and systematic hedge funds. These organizations operate at the absolute bleeding edge of technology, treating the global financial markets as a complex data science problem. They are deploying massive compute clusters and proprietary machine learning models to trade billions of dollars daily. If you're an engineer who cares about nanoseconds, cache locality, and architecting petabyte-scale infrastructure, this is the pinnacle of technical challenge.

What You'll Work On

• Architect Low-Latency Infrastructure: Design and implement the critical path for trading execution, building order management systems and market connectivity layers where performance is measured in microseconds.
• Scale Data Pipelines: Engineer elegant, distributed systems capable of ingesting and processing petabytes of market and alternative data, ensuring absolute consistency for research teams.
• Operationalize Machine Learning: Bridge the gap between research and production by deploying complex ML models into live environments under strict latency constraints.
• Optimize the Stack: Go "close to the metal" to optimize performance across networking, I/O, and compute layers, squeezing maximum efficiency out of hardware.
• Build World-Class Observability: Create robust monitoring and telemetry systems to provide real-time insights into pipeline health, trading activity, and model behavior.
• Work with the Best: Work side-by-side with world-class researchers and mathematicians to translate theoretical strategies into production-grade code.

• Polyglot Mastery: Exceptional command of at least one major systems language (C++, Rust, Java) for low-latency components, or Python for data and ML workflows.
• Distributed Systems Expertise: Deep experience building high-throughput, fault-tolerant systems using modern messaging standards (e.g., Kafka, ZeroMQ, NATS).
• Systems-Level Intuition: You are comfortable debugging and profiling at the OS level, understanding memory management, CPU architecture, and network stack optimization.
• Data Infrastructure: Familiarity with the modern data stack, including time-series databases, object stores, and streaming frameworks like Apache Flink or Spark Streaming.
• Engineering Rigor: You write clean, testable, and reliable code. You understand that in this environment, a system failure can cost millions in seconds.
• Bonus - ML Engineering: Experience with model serving, feature stores, or integrating ML pipelines into live production systems is highly valued. (Note: Previous finance experience is not required).