Software Engineer – Research Technology

About the role

DRW is looking for a Software Engineer to join a small, high‑impact team building the platform that powers researchers running compute‑heavy analyses on massive event‑stream datasets. You will work on a modern C++ core, Python tooling, and distributed HPC orchestration to deliver fast, reliable data and simulation capabilities.

Key responsibilities

• Design, build, and maintain high‑performance, scalable software and data systems for 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) for rapid iteration.
• 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.

Required profile

• 2+ years of professional experience building large‑scale, high‑performance systems with daily use of modern C++ (>=C++17) and Python.
• Strong computer‑science fundamentals: data structures, algorithms, networking, operating systems, concurrency, and system design.
• Data‑engineering fluency, including schema design, storage formats, compression, I/O trade‑offs, and pipelines handling hundreds of terabytes (Parquet/Arrow or equivalents).
• Experience designing and operating services or platforms used by other technical users in data‑intensive environments.
• Proven ability to ship production software safely and repeatedly with a data‑driven quality focus.
• Excellent written and verbal communication, collaborative mindset, and empathy.

Desirable / nice-to-have

• Rust experience alongside C++ and Python.
• Experience with cluster‑scale compute scheduling (Slurm, Kubernetes, Ray, Spark, or custom schedulers).
• GPU programming experience.
• Familiarity with machine‑learning / deep‑learning frameworks.
• Prior finance or market‑data experience, including low‑level market connectivity.
• Background in network packet processing, deterministic simulation, or game‑engine style replay systems.