ML Platform Engineer: GPU-Accelerated, Scalable Research

Trading Interview; Singapore; Hybrid; SGD 230,000 - 307,000; Full time

Benefits offered by this job

Generous paid time off

Hybrid working opportunities

Global health and welfare benefits

Free breakfast, lunch and snacks

Job summary

Tower Research Capital is a leading quantitative trading firm with a 25+ year track record, focused on building high-performance machine learning research infrastructure. The role involves architecting scalable ML platforms, enabling large-scale experiments, and collaborating with researchers to translate workflows into robust capabilities.

You will work across on-prem HPC and multi-cloud environments, optimizing pipelines, observability, and resource management while maintaining high

Qualifications

• 2+ years of experience designing and building large-scale distributed systems, ideally in support of research or data-intensive workloads.
• Strong programming experience in Python, with a focus on writing clean, maintainable, and high-performance code.
• Experience developing and operating applications on Linux-based HPC clusters and/or cloud platforms.
• Solid understanding of distributed computing concepts, parallel processing, and resource management.
• Experience with GPU-based workloads and familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX).
• Experience optimizing data pipelines and handling large-scale structured and unstructured datasets.
• Strong troubleshooting skills with the ability to debug complex, cross-layer system issues.
• Ability to work independently in a fast-paced, research-driven environment.
• Strong communication skills and experience collaborating directly with researchers or data scientists.

Responsibilities

• Architecting and developing the next generation of Tower’s machine learning research platform, with an emphasis on scalability, reliability, observability, and reproducibility.
• Building infrastructure that enables large-scale experimentation, model training, and simulation across on-premises HPC and multi-cloud environments.
• Partnering closely with quantitative researchers to understand evolving research workflows and translate them into robust platform capabilities.
• Designing and optimizing distributed training pipelines for high-throughput, GPU-accelerated workloads.
• Improving experiment management, model versioning, artifact tracking, and data lineage to ensure transparent and reproducible research.
• Developing tools and frameworks that streamline feature engineering, dataset generation, and large-scale backtesting.
• Leading initiatives to improve compute efficiency, resource scheduling, and workload isolation across heterogeneous environments.
• Enhancing platform observability, including metrics, logging, tracing, and debugging capabilities tailored to ML workloads.
• Supporting rapid iteration by implementing features and fixes on tight timelines while maintaining high engineering standards.
• Contributing to long-term architectural decisions that enable the platform to scale with increasing data volumes and model complexity.

Skills

Python

Distributed systems

Linux HPC

GPU ML

PyTorch

TensorFlow

JAX

Data pipelines

Troubleshooting

Independent work

Tools

Docker

Kubernetes

Cloud platforms

Workflow orchestration

Job description

Tower Research Capital is a leading quantitative trading firm with a 25+ year track record, focused on building high-performance machine learning research infrastructure. The role involves architecting scalable ML platforms, enabling large-scale experiments, and collaborating with researchers to translate workflows into robust capabilities.

You will work across on-prem HPC and multi-cloud environments, optimizing pipelines, observability, and resource management while maintaining high