GPU Performance Engineer | Experienced Hire

Trading Interview; Northern (KY); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Susquehanna is seeking a GPU Performance Engineer to build highly optimized CUDA kernels for low-latency inference. This role targets workloads where standard runtimes fall short, demanding custom kernels, memory layouts, and execution strategies to gain meaningful performance.

You will partner with quantitative researchers to identify bottlenecks, translate models into efficient GPU implementations, and push end-to-end latency improvements in production systems.

Qualifications

• Expert in writing and optimizing CUDA kernels.
• Strong C/C++ programming experience.
• Deep understanding of GPU architecture and memory hierarchy.
• Ability to reason about numerical stability and hardware efficiency.
• Experience with low-level systems and performance analysis.

Responsibilities

• Design, implement, and optimize custom CUDA kernels for latency-critical inference workloads.
• Develop fine-grained GPU implementations tailored to model structures.
• Analyze models to identify bottlenecks and opportunities for parallelization.
• Collaborate with quantitative researchers to translate models into high-performance pipelines.
• Profile, benchmark, and improve end-to-end inference latency and throughput.

Skills

CUDA kernels

C/C++

GPU architecture

numerical stability

low-level systems

Job description

Job Type Full-time

Posted 5 months ago

The role

Job description

Overview

We are looking for aGPU Performance Engineerto build highly optimized CUDA kernels for low-latency inference. This role is focused on workloads where off-the-shelf runtimes and vendor libraries do not fully exploit the structure of the model, and where custom kernels, memory layouts, and execution strategies can deliver meaningful gains.

You will work closely with quantitative researchers and engineers to understand model structure,identifycomputational bottlenecks, and turn mathematical ideas into production-grade GPU implementations. You will use your understanding of GPU hardware to help shape models that are both mathematically effective and efficient to run. The problems span compact neural networks, tree-based models, and other structured inference workloads where latency, throughput, and efficiency all matter.

This role is a strong fit for someone who enjoys low-level optimization, performance analysis, and translating abstract models into hardware-efficient code.

What you'll do

• Design, implement, and optimize custom CUDA kernels for latency-critical inference workloads
• Develop fine-grained GPU implementations tailored to specific model structures
• Analyze quantitative research models and computational bottlenecks to identify opportunities for parallelization and hardware-efficient execution
• Collaborate directly with quantitative researchers to translate mathematical models into high-performance compute pipelines
• Optimize end-to-end inference performance through kernel tuning, memory-layout design, execution strategy, I/O optimization, and precision tradeoffs
• Profile and benchmark GPU performance
• Improve latency and throughput in production inference systems
• Contribute to GPU architecture decisions and performance best practices

What we're looking for

• Strong proficiency in writing and optimizing CUDA kernels
• Solid programming experience in C/C++ (preferred)
• Deep understanding of GPU architecture, including memory hierarchy, SIMT execution, occupancy, and latency/throughput tradeoffs
• Ability to reason about numerical stability, precision, performance tradeoffs, and how model design choices affect hardware efficiency
• Strong problem-solving skills and comfort working with low-level systems

Preferred qualifications

• PhD in mathematics, physics, computer science, engineering, or related quantitative field
• Strong background in linear algebra, probability, numerical methods, or scientific computing
• Experience working with quantitative research teams or financial models
• Demonstrated ability to improve real-world inference performance beyond baseline framework or library implementations
• Familiarity with PTX-level behavior, tensor core utilization, or architecture-specific tuning
• Exposure to ONNX Runtime, TensorRT, Triton, TVM, or similar systems
• Exposure to neural networks, tree-based models (e.g., LightGBM), state space models (e.g., Mamba architectures), and experience with kernel fusion, custom operators, model compilation, or graph-level optimization

About Susquehanna

Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.

• Strong proficiency in writing and optimizing CUDA kernels
• Solid programming experience in C/C++ (preferred)
• Deep understanding of GPU architecture, including memory hierarchy, SIMT execution, occupancy, and latency/throughput tradeoffs
• Ability to reason about numerical stability, precision, performance tradeoffs, and how model design choices affect hardware efficiency
• Strong problem-solving skills and comfort working with low-level systems

Preferred qualifications

• PhD in mathematics, physics, computer science, engineering, or related quantitative field
• Strong background in linear algebra, probability, numerical methods, or scientific computing
• Experience working with quantitative research teams or financial models
• Demonstrated ability to improve real-world inference performance beyond baseline framework or library implementations
• Familiarity with PTX-level behavior, tensor core utilization, or architecture-specific tuning
• Exposure to ONNX Runtime, TensorRT, Triton, TVM, or similar systems
• Exposure to neural networks, tree-based models (e.g., LightGBM), state space models (e.g., Mamba architectures), and experience with kernel fusion, custom operators, model compilation, or graph-level optimization

About Susquehanna

Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.