Research Platform Engineer

Tower Research Capital is a leading quantitative trading firm founded in 1998. Tower has built its business on a high-performance platform and independent trading teams. We have a 25+ year track record of innovation and a reputation for discovering unique market opportunities.

Tower is home to some of the world’s best systematic trading and engineering talent. We empower portfolio managers to build their teams and strategies independently while providing the economies of scale that come from a large, global organization.

Engineers thrive at Tower while developing electronic trading infrastructure at a world class level. Our engineers solve challenging problems in the realms of low-latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing investment in top engineering talent and technology ensures our platform remains unmatched in terms of functionality, scalability and performance.

At Tower, every employee plays a role in our success. Our Business Support teams are essential to building and maintaining the platform that powers everything we do — combining market access, data, compute, and research infrastructure with risk management, compliance, and a full suite of business services. Our Business Support teams enable our trading and engineering teams to perform at their best.

At Tower, employees will find a stimulating, results-oriented environment where highly intelligent and motivated colleagues inspire each other to reach their greatest potential.

As part of Tower Research's Core Engineering team, you will develop a multi-tenant research compute platform capable of dynamically orchestrating large scale ML workloads across a hybrid compute infrastructure of GPUs and CPUs. Your primary mission is to work closely with Quant Researchers, Portfolio Managers, and Infrastructure teams, to build a unified, elastic, and multi-tenant research substrate.

• Elevating Researcher Experience: Design intuitive platform abstractions, APIs, scheduler wrappers, and a durable job control plane so researchers can seamlessly launch simulations, distributed training runs, and complex research pipelines without incurring infrastructure overhead

• Architecting Multi-Tenant Scheduling & Isolation: Build a multi-tenant compute substrate combining HPC-grade batch scheduling (gang scheduling, topology awareness, fair share) with cloud-native flexibility, enforcing strict tenant isolation across trading desks

• Enabling Multi-Datacenter & Multi-Cloud Compute Portability: Establish infrastructure agnostic execution abstractions that enable compute workloads to run transparently across multiple on-premise datacenters or burst into external cloud providers

• Standardizing Workflow Orchestration: Evaluate, select, and integrate production-grade job graph orchestration engines to automate multi-stage feature, training, and backtesting pipelines

• Building Fault-Tolerant Research Pipelines: Implement automated failure detection, retry-on-fault mechanisms, and high-performance checkpointing to ensure long-running distributed jobs survive hardware degradation and faults without manual intervention

• Optimizing Data-Paths: Profile and eliminate I/O bottlenecks, ensuring distributed ML workloads align with high-speed network fabrics and high-performance storage

• Delivering Observability & Cost Transparency: Implement comprehensive telemetry to track compute utilization, queue pressure, and GPU/CPU cost attribution, giving Portfolio Managers and Senior Management clear visibility into resource consumption and ROI

Technical Requirements

• HPC Administration: Deep experience with HPC job schedulers (e.g.,