High-Performance Software Engineer, Research Tech

P2P; Singapore; On-site; SGD 152,000 - 241,000; Full time

Job summary

DRW is seeking a Software Engineer, Research Technology to build the software platform powering researchers analyzing large event-stream datasets in finance. You will contribute to ingestion, simulation, and tooling in C++ and Python, with mentoring to accelerate growth.

Work spans raw data ingestion, distributed HPC orchestration, and research tooling to enable researchers to slice, backtest, and iterate quickly. Strong fundamentals and collaboration are essential.

Qualifications

• 2+ years of professional experience building large-scale high-performance systems.
• Strong CS fundamentals: data structures, algorithms, networking, OS, concurrency, and system design.
• Data engineering fluency: designing schemas, formats, I/O trade-offs, and pipelines handling hundreds of TB.

Responsibilities

• Design, build, and maintain high-performance, scalable software and data systems used by quant researchers and trading teams.
• Implement raw exchange data pipelines in modern C++ and operate them at high-throughput scale.
• Orchestrate and improve reliability of data and compute pipelines on HPC clusters.
• Create ad-hoc computation frameworks and research tooling (Python + C++ integrations).
• Develop and maintain simulation frameworks tightly integrated with HFT/live trading platforms.
• Support training and deployment of quantitative models used in trading.
• Monitor, manage, and troubleshoot distributed platforms.
• Optimize codebases for performance, reliability, and resource efficiency across the full stack.

Skills

C++17

Python

Data engineering

Distributed systems

HPC

Communication

Tools

Slurm

Kubernetes

Ray

Spark

Job description

DRW is seeking a Software Engineer, Research Technology to build the software platform powering researchers analyzing large event-stream datasets in finance. You will contribute to ingestion, simulation, and tooling in C++ and Python, with mentoring to accelerate growth.

Work spans raw data ingestion, distributed HPC orchestration, and research tooling to enable researchers to slice, backtest, and iterate quickly. Strong fundamentals and collaboration are essential.