Software Engineer - Research Technology

• →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.
• →Monitor, manage, and troubleshoot distributed platforms.
• →Optimize codebases for performance, reliability, and resource efficiency across the full stack.

• 2+ 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.
• Data engineering fluency: designing schemas, choosing storage formats, understanding compression and I/O trade-offs, and operating pipelines that write and read hundreds of TB. Comfortable reasoning about columnar formats (Parquet / Arrow) or their equivalents in your domain (event logs, telemetry, replays).
• Experience designing and operating services or platforms used by other technical users in data-intensive environments.
• Comfortable supporting internal users and iterating on ergonomics and workflows.
• Demonstrated ability to ship production software safely and repeatedly, with an obsession for data driven quality.
• Excellent written and verbal communication; collaborative mindset and empathy

• Rust experience alongside C++ and Python.
• Experience running compute at cluster scale: job scheduling, resource management, retries, and reliability. Slurm, Kubernetes, Ray, Spark, or custom internal schedulers all count.
• GPU programming experience.
• Familiarity with ML/Deep Learning frameworks.
• Prior finance or market-data experience, including low-level market connectivity.
• Historical network / packet processing (telco, network appliances, packet-capture replay infrastructure) - any exposure.
• Deterministic simulation and replay systems (game engines, cell simulators, distributed-systems testing frameworks) - any exposure.
• Dev-productivity or platform-engineering for internal users (making other engineers or researchers faster and safer) - any exposure, including internship or open-source work.

We evaluate CS fundamentals, ability to reason about correctness and performance under real constraints, code quality, communication, and clear signs of growth (asks good questions, learns fast, takes feedback well). Depth of prior industry experience is not the bar; trajectory is. We do not test market-microstructure knowledge in loops; that is taught on the job. Candidates from bigtech, telco, gaming, or research-computing backgrounds have transferable skills for this role and are actively encouraged to apply.