Machine Learning Performance Engineer (Inference)

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About the role

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. Summary: As part of Tower Research's Core Engineering team, you will bridge the gap between quantitative research and high-performance production systems, architecting inference pipelines that operate at the physical limits of hardware. Your objective will be to drive the speed, efficiency, and reliability of our ML inference pipelines to their absolute limits, ensuring our predictive models consistently achieve microsecond-level latency. Responsibilities: • Benchmarking & Strategy: • Lead the technical evaluation of diverse inference platforms - ranging across CPUs, GPUs, and FPGAs - to guide Tower's infrastructure deployment decisions. • System Architecture Optimization: • Analyze and enhance execution across deep memory hierarchies to maximize resource utilization and parallel processing. You will assess and resolve memory subsystem and interconnect bottlenecks across the end-to-end inference lifecycle. • Infrastructure & Deployment Feasibility: • Collaborate with Infrastructure teams to understand thermal, power, and operational constraints of hardware platforms to design inference strategies for our latency-critical trading strategies that fit within those envelopes. • GPU Kernel Development: • Develop highly optimized kernels and integrate specialized performance libraries to extract maximum computational throughput from the underlying silicon. • Model Optimization & Deployment: • Implement advanced model reduction techniques (quantization, pruning, distillation) to ensure compact memory footprints and numerical stability. Prioritize optimization for low-latency, event-level inference workloads to meet real-time trading requirements. • Cross-Functional Collaboration: • Collaborate closely with ML Researchers, HPC Engineers, FPGA Engineers, and Datacenter Engineers to bring to fruition target deployments. Qualifications: • 2+ years of experience optimizing deep learning inference in latency-sensitive or high-throughput production environments, in any domain. • ML Frameworks: Deep expertise in lower-level ML framework development (PyTorch/JAX), paired with strong Python/C++ skills and a thorough understanding of mixed-precision computation. • Kernel Development & Optimization Tooling: Proven experience in custom GPU kernel

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