Distributed Systems Engineer

Distributed Systems Engineer - Global Quant Trading Firm | Up to £400k TC A globally recognised and fast-growing quantitative trading firm are searching for a Distributed Systems Engineer to help design and optimise their distributed computing environment. You’ll be working in a highly complex and technical environment, contributing to the performance and scalability of large-scale systems that underpin the firm's quantitative research and trading platforms. The team has a deep engineering culture, flat structure, and a strong focus on technical excellence. Below I have included a breakdown of the role, company, and requirements. Please review and if the opportunity seems like a good fit share your CV! Role: Architect and optimise large-scale compute-intensive workloads spanning significant numbers of nodes and concurrent tasks Design, build, and manage systems with tools like Ray and YellowDog Optimise application performance on distributed platforms Provide architectural guidance on distributed computing design and development Drive efficiency and scalability across the platform, with a focus on ML pipeline execution Company: Technology-led culture – Drives both trading and internal investment decisions c.1,000 employees – Large enough for scale, small enough for individual impact New state-of-the-art London HQ – Core hub for engineering and trading, Free On-Site Gym Flat structure – Direct access to senior engineers and C-level leaders Strong Glassdoor rating Great work life balance (frequently quoted on Glassdoor) - Free Breakfast and Lunch, 2 days per week WFH Competitive Compensation - Year 1 guaranteed bonus, 13% pension, Potential for Sign-On Bonuses Requirements: Understanding of Loosely/Tightly coupled workloads HPC platform experience Job/Resource scheduling experience i.e. Yellowdog Cloud platform proficiency (any provider) Experience with large scale systems (1k Nodes, 10k tasks) Experience monitoring/troubleshooting a distributed environment Advance Ray experience for ML pipelines, tuning, distributed execution Python and Conda proficiency Docker Kubernetes experience Knowledge of networking (TCP/IP, UDP/IP, LAN/WAN) Identify and access management knowledge