DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.
Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.
We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.
Build and operate the platform powering compute-intensive analysis of large event-stream datasets. The work resembles a game engine’s replay loop, a telecom packet pipeline, or a streaming analytics platform, applied to financial market data.
Our stack includes modern C++ for throughput-critical ingestion and simulation, Python with C++ bindings for research tooling, and distributed computing on HPC clusters. Your work will span exchange-data ingestion, HPC orchestration, simulation, and research tooling.
Trading experience is not required; we provide comprehensive onboarding.
Key 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 that let researchers slice, backtest, and iterate rapidly (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.
- Optimize codebases for performance, reliability, and resource efficiency across the full stack.
- Work primarily as a hands-on individual contributor while mentoring junior team members as needed.
Required qualifications
- 7+ years of professional experience building large-scale, high-performance systems; daily use of modern C++ (>=17) and Python expected.
- Strong CS fundamentals: data structures, algorithms, networking, OS, concurrency, and system design.
- Experience running compute at cluster scale: job scheduling, resource management, retries, and reliability. Slurm, Kubernetes, Ray, Spark, or custom internal schedulers all count.
- Proven data-engineering experience: schema design, storage formats, compression, I/O trade-offs, and pipelines processing hundreds of terabytes.
- Experience with columnar formats such as Parquet or Arrow, or comparable domain-specific formats used for event logs, telemetry, or replays.
- Experience designing and operating services or platforms used by other technical users in data-intensive environments.
- Demonstrated ability to ship production software safely and repeatedly, with an obsession for data driven quality.
Desirable / nice-to-have
- Rust experience alongside C++ and Python.
- Slurm or other cluster scheduler expertise.
- Familiarity with ML/Deep Learning frameworks.
- Prior finance or market-data experience, including low-level market connectivity.
- GPU programming experience.
- Historical network / packet processing (telco, network appliances, packet-capture replay infrastructure).
- Deterministic simulation and replay systems (game engines, cell simulators, distributed-systems testing frameworks).
- Dev-productivity or platform-engineering for internal users (making other engineers or researchers faster and safer, at scale).
What we evaluate in our interview loop
We evaluate systems-design judgement on data-intensive problems, ability to reason about correctness under concurrency and failure, code quality, and communication. We do not test market-microstructure knowledge in loops; that is taught on the job. Candidates from Big Tech, Telco, Gaming, or Research-computing backgrounds have transferable skills for this role and are actively encouraged to apply.
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