Quantitative trading firm running low-latency, FPGA-accelerated trading systems
Tower Research Capital is a New York-based quantitative trading firm whose stack pairs C++, Rust and FPGA for latency-critical execution with Python, Polars, PyTorch and JAX for research. Adoption of Bazel, CMake, Docker and Kubernetes points to a push toward standardized builds and containerized compute, alongside Slurm and WEKA for petabyte-scale research workloads. Hiring is engineering-heavy, mostly mid-level, and currently decelerating.
Tower Research Capital is a privately held quantitative trading firm founded in 1998 and headquartered in New York, NY. It operates a proprietary trading platform supported by independent trading teams that develop algorithmic strategies. The firm has 1,001–5,000 employees across more than a dozen offices worldwide, and hires in the United States, Canada, United Kingdom, Netherlands, India, Singapore, China, Australia and the Cayman Islands. Its workforce spans engineering, research, support, operations, finance, security and HR functions.
Tower uses C++, Rust, Python and FPGA for trading systems, plus NumPy, Pandas, Polars, PyTorch, TensorFlow and JAX for research, with Kubernetes, Slurm, WEKA, Terraform and AWS, GCP and Azure for infrastructure.
Tower hires in the United States, Canada, United Kingdom, Netherlands, India, Singapore, China, Australia and the Cayman Islands, with engineering making up most of its open roles.
Current work includes its high-frequency trading platform, low-latency production systems, historical market data access, backtest and research framework improvements, and risk-management tools.
Other companies in the same industry, closest in size
Tower Research Capital's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.