C++ Engineer

OverviewJoin a compact engineering group at a fast-moving, data-driven trading organization focused on scientific research and automated execution. This hands-on role sits at the intersection of software engineering and quantitative research: you’ll build the plumbing that makes large-scale experiments possible, accelerate model evaluation, and keep production trading systems reliable and responsive. Great for engineers who enjoy systems work, tight performance budgets, and close collaboration with researchers.What you’ll ownArchitect, implement, and operate compute and data infrastructure that supports research experiments, backtests, and live strategy execution.Build high-throughput data pipelines and fast-loading data caches so researchers and engines get clean inputs with minimal delay.Implement components and optimizations to reduce end-to-end latency for model evaluation and order-routing logic.Create tools and automation to simplify model training, run orchestration, testing, and deployment.Work day-to-day with model authors and traders to turn algorithmic requirements into practical, maintainable engineering solutions.Maintain runbooks, observability, and clear internal documentation so teams can reproduce results and recover quickly.Why this role mattersYou’ll shorten research cycles by improving data and compute responsiveness.Your work directly impacts simulation fidelity and live trading reliability.Small team, big influence: your changes will be shipped and relied upon immediately.Must-have skills & experienceDegree in Computer Science, Engineering, Mathematics, or similar technical field (B.S./M.S.).Minimum 2 years building production C++ systems (C++17 or newer), with strong fundamentals in algorithms, memory management, and concurrency.Demonstrated ability to design and tune systems for low-latency and high throughput.Comfortable reasoning about numeric workloads, I/O patterns, and performance trade-offs.Strong plusesPractical experience building data ingestion pipelines or compute orchestration for research or analytics.Familiarity with Python for tooling, testing, or data manipulation.Background in financial technology, scientific computing, or other latency-sensitive domains.Culture & environmentCollaborative, engineering-first environment; small cross-functional squads.Emphasis on measurable improvements, reproducibility, and operational discipline.Hands-on ownership — you’ll ship code, run systems, and help steer priorities.