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