Jobs at Qube Research & Technologies

Operate and maintain large-scale, high-performance storage infrastructure supporting HPC research and trading environments. Responsibilities include storage administration, performance benchmarking and tuning, incident management, user support, vendor coordination, and infrastructure deployment, upgrades, and maintenance. The role also uses DevOps tooling, infrastructure monitoring, Linux systems, storage protocols, and automation technologies across petabyte-scale environments.

Support the full trade lifecycle for energy commodities, including trade capture, affirmation, reconciliation, confirmation, and settlement. Investigate operational breaks, resolve queries, and coordinate with traders, risk, finance, treasury, technology, brokers, and clearing parties. The role supports listed and OTC derivatives, improves processes and controls, and drives automation. As the business expands, it will help establish operational processes for physical energy and gas trading, including Japanese markets.

Develop scalable services for a next-generation data platform supporting incremental ingestion, aggregation, indexing, distributed systems, and large-scale data processing. Responsibilities include automated testing, maintaining delivery and integration pipelines, building reusable libraries, and collaborating with engineers and researchers on framework features. The role requires proficiency in Rust, Go, C++, or Java, plus distributed systems, multithreading, SQL, relational databases, cloud technologies, and effective communication.

Develop scalable data platform services for incremental ingestion, aggregation, and indexing of diverse data sources. Build distributed systems and large-scale data processing capabilities, write automated tests, maintain delivery and integration pipelines, develop reusable libraries, and collaborate with engineers and researchers on framework features. The role emphasizes reliability, scalability, performance, and usability, using Rust or comparable experience in Go, C++, or Java.

Design, implement, and review high-performance data infrastructure services and clients. Apply coding, testing, and system design best practices; collaborate with stakeholders to address technical and business needs; and investigate, debug, and resolve system issues. Work with large-scale data systems involving real-time publish-subscribe architecture, sharded databases, caching, multithreading, asynchronous I/O, and big data technologies.

Manage large datasets across research and trading platforms by designing data stores and APIs, monitoring data-process health and performance, delivering production-ready datasets, and supporting researchers and traders. The role involves Python-based data engineering in a low-latency environment, collaboration with technical and investment professionals, and developing expertise in high-performance real-time trading systems.

Design and build ultra-low-latency FPGA hardware systems for quantitative trading platforms. Develop custom logic for fast market-data processing and order execution using SystemVerilog, C++, and Python. Optimize designs for performance, resource usage, power efficiency, and reliability while collaborating with experienced engineers. The 3–6 month 2027 program is intended for penultimate or final-year Bachelor's, Master's, or PhD students and may lead to a graduate position.

Design, develop, deploy, and support cloud-native commodities research and trading applications. Build Python and React/TypeScript analytical tools, data pipelines, backtesting frameworks, APIs, modeling workflows, and scalable infrastructure. Process market, weather, geospatial, remote-sensing, and alternative datasets while ensuring data quality, reproducibility, monitoring, and point-in-time correctness. Collaborate with researchers, traders, data engineers, and infrastructure teams to productionize exploratory workflows.

Three- to six-month 2027 internship for penultimate or final-year students supporting on-premises infrastructure and cloud platforms for quantitative trading. Responsibilities may include Linux systems analysis, hardware performance optimization, scalable cloud infrastructure, data pipelines, infrastructure automation, monitoring, and DevOps practices. Candidates should demonstrate programming or scripting ability, technology enthusiasm, curiosity, and a results-oriented mindset. The process includes an application, coding assessment, and technical interviews or assessment centre.

Administer and support JFrog and Atlassian development tooling, troubleshoot incidents and CI/CD integrations, monitor platform health, and improve reliability. The role manages upgrades, configuration changes, performance tuning, documentation, automation, migrations, and on-call support while collaborating with engineering, infrastructure, security, and DevOps teams.

Three- to six-month 2027 internship for penultimate- or final-year Bachelor's, Master's, or PhD students. Quantitative developers build tools, platforms, and infrastructure supporting quantitative research and live trading. Software engineers develop high-performance trading, execution, risk, analytics, and market connectivity systems. Candidates contribute to technically challenging projects, optimize performance, and collaborate with researchers, traders, and engineers in a rigorous environment.

Partner with traders and quantitative researchers to analyze PnL, positions, strategy performance, and trading anomalies. Investigate discrepancies, perform reconciliations across trading, valuation, and risk systems, and develop dashboards and analytical tools. Contribute to automated reporting platforms, scalable data pipelines, and data architecture improvements while collaborating with technology and front-office teams.

The Senior Detection and Response Engineer will enhance security operations, monitor threats, manage incidents, and develop detection capabilities while collaborating with various teams.

The Data Support Specialist provides support to researchers and traders, manages data access, collaborates on data issues, and contributes to documentation and training.

The role involves designing and optimizing C++ systems for trading and market data while collaborating with FPGA engineers on low-latency architectures.

Design and develop scalable reference-data services, automate lifecycle events, build/internal and external reporting (real-time and batch), collaborate with research and execution teams, maintain CI/CD and automated testing, and integrate Python and C# components to support trading and research workflows.

Develop and enhance a Python library for research and trading strategies, focusing on end-to-end solutions, backtesting, validation, and production scaling.

Develop scalable cloud-native platforms for real-time tick data. Collaborate with researchers, architect systems, and build distributed data architectures.

Manage and evolve Linux platforms for global trading, lead automation frameworks, ensure performance, and support build/release management.

Develop and implement high-frequency and low latency algo trading strategies for APAC markets, collaborating with traders and researchers.