Senior Software Engineer - Research Technology

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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