Teams at Tower Research Capital

Optimize large-scale machine learning training systems across CPUs, GPUs, and accelerators. Responsibilities include benchmarking, distributed-training optimization, GPU kernel and framework development, end-to-end pipeline efficiency, numerical optimization, and training infrastructure. The role partners with ML researchers, quantitative researchers, HPC engineers, systems engineers, and hardware specialists to improve performance, scalability, utilization, reproducibility, and cost across on-premises and cloud environments.

Build and maintain scalable batch and real-time data pipelines for financial and alternative datasets. Productionize AI/ML workflows for document extraction, classification, and data processing; own model evaluation, monitoring, and human review. Design investment data models, validation, anomaly detection, and reconciliation systems. Partner with quants and data scientists to operationalize research signals, onboard vendors and datasets, integrate APIs, and improve data reliability using modern cloud, streaming, orchestration, and warehouse technologies.

Analyze and improve structured and unstructured financial data pipelines using Python, SQL, statistical methods, machine learning, and LLMs. Responsibilities include data cleansing, validation, anomaly detection, reconciliation, dataset onboarding, API integration, model evaluation, workflow monitoring, documentation, and root-cause investigation. The role also involves explaining technical findings, managing assigned datasets, and supporting reliable production data processes in a quantitative trading environment.