Research Platform - Software Engineer

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset, which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

As part of our Research Platforms team, you will design and build the distributed systems that enable large-scale computational workloads to run efficiently, securely, and reliably across our global research infrastructure.

This role sits at the intersection of distributed systems, platform engineering, and high-performance computing. You will work on the foundational services researchers depend on every day, including workload orchestration, scheduling, security, observability, and platform tooling.

• →Design, build, and operate distributed services that manage compute workloads and data access across large-scale infrastructure
• →Develop platform APIs and tooling that improve the productivity of quantitative researchers
• →Improve the reliability, scalability, and performance of compute-intensive research workflows
• →Build solutions for scheduling, resource management, observability, and security
• →Partner closely with researchers and infrastructure teams to translate complex requirements into scalable platform capabilities
• →Take ownership of systems end-to-end, from design and implementation through production operation

• BS, MS, or PhD in Computer Science or a related field, or equivalent practical experience
• 5+ years of experience building and operating large-scale distributed systems in production
• Strong proficiency in C++, Java, Rust, Go, or similar systems languages
• Strong understanding of distributed systems, concurrency, performance, reliability, and fault tolerance
• Experience designing services, platforms, or infrastructure used by other engineers

• Experience with internal developer platforms, cloud infrastructure, orchestration systems, or HPC environments
• Familiarity with GPU-accelerated workloads and modern ML frameworks such as PyTorch or TensorFlow
• Experience supporting researchers, data scientists, or other highly technical users
• Exposure to quantitative finance or large-scale data-intensive environments