Luma AI
Research Scientist / Engineer – Performance Optimization
Luma AI, Palo Alto, California, United States, 94306
Research Scientist / Engineer – Performance Optimization
Join to apply for the
Research Scientist / Engineer – Performance Optimization
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Luma AI About The Role The Performance Optimization team at Luma is dedicated to maximizing the efficiency and performance of our AI models. Working closely with both research and engineering teams, this group ensures that our cutting-edge multimodal models can be trained efficiently and deployed at scale while maintaining the highest quality standards. Responsibilities
Profile and optimize GPU/CPU/Accelerator code for maximum utilization and minimal latency Write high-performance PyTorch, Triton, CUDA, deferring to custom PyTorch operations if necessary Develop fused kernels and leverage tensor cores and modern hardware features for optimal hardware utilization on different hardware platforms Optimize model architectures and implementations for distributed multi-node production deployment Build performance monitoring and analysis tools and automation Research and implement cutting-edge optimization techniques for transformer model Experience Expert-level proficiency in Triton/CUDA programming and GPU optimization Strong PyTorch skills Experience with PyTorch kernel development and custom operations Proficiency with profiling tools (NVIDIA Nsight, torch profiler, custom tooling) Deep understanding of transformer architectures and attention mechanisms (Preferred) Experience with compilers/exporters such as torch.compile, TensorRT, ONNX, XLA (Preferred) Experience optimizing inference workloads for latency and throughput (Preferred) Experience with Triton compiler and kernel fusion techniques (Preferred) Knowledge of warp-level intrinsics and advanced CUDA optimization
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Join to apply for the
Research Scientist / Engineer – Performance Optimization
role at
Luma AI About The Role The Performance Optimization team at Luma is dedicated to maximizing the efficiency and performance of our AI models. Working closely with both research and engineering teams, this group ensures that our cutting-edge multimodal models can be trained efficiently and deployed at scale while maintaining the highest quality standards. Responsibilities
Profile and optimize GPU/CPU/Accelerator code for maximum utilization and minimal latency Write high-performance PyTorch, Triton, CUDA, deferring to custom PyTorch operations if necessary Develop fused kernels and leverage tensor cores and modern hardware features for optimal hardware utilization on different hardware platforms Optimize model architectures and implementations for distributed multi-node production deployment Build performance monitoring and analysis tools and automation Research and implement cutting-edge optimization techniques for transformer model Experience Expert-level proficiency in Triton/CUDA programming and GPU optimization Strong PyTorch skills Experience with PyTorch kernel development and custom operations Proficiency with profiling tools (NVIDIA Nsight, torch profiler, custom tooling) Deep understanding of transformer architectures and attention mechanisms (Preferred) Experience with compilers/exporters such as torch.compile, TensorRT, ONNX, XLA (Preferred) Experience optimizing inference workloads for latency and throughput (Preferred) Experience with Triton compiler and kernel fusion techniques (Preferred) Knowledge of warp-level intrinsics and advanced CUDA optimization
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