Distributed Systems Software Engineer- Leading Quantitative Trading Firms

• Strong software engineering fundamentals
• Experience designing complex distributed systems
• Deep understanding of concurrency and parallelism
• Knowledge of reliability, consistency and failure modes
• Strong systems thinking
• Experience with C++, Java, Python, Go, Rust or similar

Prior trading experience is not required for many of these teams. The firms I represent are particularly interested in engineers from Big Tech, infrastructure companies, databases, cloud platforms, HPC, developer infrastructure and other technically demanding environments.

The key difference versus many large technology companies is proximity to impact: small teams, significant ownership, extremely capable colleagues and systems where improvements in performance or reliability can have an immediate measurable effect.

• Competitive salary
• London
• Permanent, full-time

Full job description

Some of the most interesting distributed systems problems in technology aren’t currently sitting inside traditional Big Tech.

I’m working with several leading quantitative trading firms building infrastructure operating at extraordinary levels of scale, throughput, latency and reliability.

Trading firms need to process global real-time data, run enormous research workloads, maintain highly available trading systems and provide researchers with access to vast amounts of compute. Current public material across the sector describes everything from distributed state-machine replication to simulations running across tens of thousands of compute cores.

Depending on the team, you could work on:

• Large-scale distributed compute
• Scheduling and workload orchestration
• Distributed storage and data platforms
• Real-time event-driven systems
• High-throughput messaging
• Fault tolerance and state replication
• Research infrastructure
• Petabyte-scale data
• HPC / CPU / GPU compute
• Observability and reliability
• Developer platforms and abstractions
• Performance engineering across compute, storage and networking