Top Tech Jobs & Startup Jobs

The Head of Infrastructure will lead APAC infrastructure engineering, ensuring reliable, low-latency infrastructure delivery, mentoring talent, and managing stakeholder relationships.

The Engineering Lead will oversee the APAC Data Engineering Platform team, responsible for developing a scalable data platform, leading team development, collaborating with stakeholders, and ensuring high engineering standards.

Design, build, deploy, and evolve high-performance trading and research platforms. Develop automated trading systems running machine-learning models, improve simulation and backtesting pipelines, partner with traders and researchers on feature engineering and experimentation, design scalable data platforms and pipelines, and monitor and troubleshoot production systems for stability and performance.

As a Quantitative Hardware Researcher, you will design experiments, analyze hardware performance, work with engineers to improve trading systems, and run tests in a lab environment.

Build and optimize machine learning infrastructure and platforms, ensuring stability, efficiency, and scalability for large-scale data model training and simulations.

The Machine Learning Modelling Engineer will leverage AI and deep learning, implementing complex models, optimizing processes, and collaborating on experiments.

As a C++ Software Engineer, you will develop and optimize systems for pricing, quoting, and executing trades in commodities, collaborating closely with traders and researchers.

Lead global sourcing strategies and end-to-end RFx for enterprise hardware (servers, storage, networking). Own supplier relationships, negotiate commercial terms, monitor supply chain risk and lifecycle planning, advise engineering stakeholders on TCO and refresh decisions, ensure procurement governance, and drive process improvements using automation and AI.

Build and extend a quantitative research and simulation framework for systematic cash equities. Develop distributed backtesting, model-training systems, curate high-quality research datasets, and collaborate with researchers to translate requirements into production-grade software. Improve researcher workflows and apply modern AI tools where valuable.

Lead and grow an AI Engineering team (10-15) to build and operate a diversified model substrate and evaluation stack: onboard open-weight and API models, optimize inference cost and routing, design rigorous model benchmarking and regression-gating, and partner with AI Risk, Procurement and global leaders to ensure safe, regionally-accessible model deployments.