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