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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.
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Who can apply
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