HeyGen - Software Engineer, GPU Performance

Los Angeles, Palo Alto, San Francisco, Toronto, Singapore3w ago

In Office APAC Artificial Intelligence Software Engineer Graphics Engineer Python Performance Management Triton CUDA C++

Requirements

• Experience optimizing GPU-based AI workloads or high-performance computing systems. • Proficiency in Python and experience with PyTorch or a similar machine learning framework. • Strong curiosity about GPU hardware, including memory bandwidth, cache behavior, tensor cores, and data movement between CPU and GPU. • Experience using profiling tools to connect hardware behavior to application-level bottlenecks and validate improvements. • Ability to turn performance experiments into reliable production changes and communicate tradeoffs clearly. • Experience with CUDA, Triton, or C++ GPU programming. • Experience optimizing video, image, audio, diffusion, or Transformer models. • Familiarity with multi-GPU inference, GPU interconnects, quantization, or large-scale model serving. • Experience building performance benchmarks or regression testing infrastructure. • Prior experience in a fast-paced technology environment. • What HeyGen Offers • Competitive salary and benefits package. • Dynamic and inclusive work environment. • Opportunities for professional growth and advancement. • Collaborative culture that values innovation and creativity. • Access to the latest technologies and tools.

Responsibilities

• Use NVIDIA Nsight Systems, Nsight Compute, and PyTorch Profiler to investigate GPU utilization, kernel execution, memory bandwidth, and CPU–GPU data movement. • Identify bottlenecks across model execution, preprocessing, and inference serving, then measure the impact of each optimization. • Improve performance through batching, scheduling, memory management, and better GPU utilization. • Develop or integrate high-performance GPU kernels when existing implementations limit performance. • Build benchmarks and automated checks that catch performance regressions across representative video workloads. • Collaborate with AI researchers and infrastructure engineers to bring optimizations into production. • Measure the effect of changes on latency, throughput, cost, and output quality.