Machine Learning Engineer (Machine Learning)

Location: Hong Kong, Hong Kong; hybrid working opportunities are available.

Company

hirify.global is a quantitative trading firm developing electronic trading infrastructure, research platforms, and financial technology at global scale.

What you will do

• Architect and develop a scalable, reliable, observable, and reproducible machine learning research platform.
• Build infrastructure for large-scale experimentation, model training, and simulation across on-premises HPC and multi-cloud environments.
• Design and optimize distributed training pipelines for high-throughput, GPU-accelerated workloads.
• Improve experiment management, model versioning, artifact tracking, and data lineage.
• Develop tools for feature engineering, dataset generation, and large-scale backtesting.
• Improve compute efficiency, resource scheduling, workload isolation, and observability across heterogeneous environments.

Requirements

• At least 2 years of experience designing and building large-scale distributed systems.
• Strong Python programming skills and the ability to write clean, maintainable, high-performance code.
• Experience operating applications on Linux-based HPC clusters and/or cloud platforms.
• Understanding of distributed computing, parallel processing, and resource management.
• Experience with GPU-based workloads and modern machine learning frameworks such as PyTorch, TensorFlow, or JAX.
• Experience optimizing data pipelines and working with large-scale structured and unstructured datasets.

Nice to have

• Experience building internal machine learning platforms or research tooling at scale.
• Familiarity with experiment tracking, workflow orchestration, and model lifecycle management.
• Experience with Docker and Kubernetes.
• Exposure to quantitative finance, simulation systems, or latency- and performance-sensitive domains.

Culture & Benefits

• Hybrid working opportunities in a collaborative, welcoming, and results-oriented environment.
• Generous paid time off policies and regional savings plans and financial wellness tools.
• Free breakfast, lunch, and snacks daily.
• In-office wellness experiences, wellness expense reimbursement, sports teams, and fitness events.
• Volunteer opportunities, charitable giving, social events, and continuous learning workshops.