GPU Performance Engineer | Experienced Hire

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 structureidentifycomputational 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 youll 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

• 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

The annual base pay range for this role is $200000 - $300000 discretionary bonus benefits. Susquehanna considers factors such as scope and responsibilities of the position work experience education/training key skills as well as market and organizational considerations when extending an offer.

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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