CUDA Kernel Architect for Low-Latency Inference

Susquehanna International Group, LLP; Hong Kong; On-site; HKD 900,000 - 1,300,000; Full time

Job summary

Susquehanna International Group, LLP is seeking a GPU Performance Engineer to build highly optimized CUDA kernels for low-latency inference. You’ll work with quantitative researchers to identify bottlenecks and translate mathematical models into production-grade GPU code, focusing on models like compact neural networks and tree-based methods where latency and throughput matter.

You should enjoy low-level optimization, performance analysis, and hardware-aware design.

Qualifications

• 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.

Responsibilities

• 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

Skills

CUDA kernels

C/C++

GPU architecture

Numerical stability

Low-level systems

Education

PhD in Mathematics, Physics, Computer Science, Engineering, or related quantitative field

Tools

ONNX Runtime

TensorRT

Triton

TVM

Job description

Susquehanna International Group, LLP is seeking a GPU Performance Engineer to build highly optimized CUDA kernels for low-latency inference. You’ll work with quantitative researchers to identify bottlenecks and translate mathematical models into production-grade GPU code, focusing on models like compact neural networks and tree-based methods where latency and throughput matter.

You should enjoy low-level optimization, performance analysis, and hardware-aware design.