Research Scientist / Engineer – Performance Optimization

Profile and optimize GPU/CPU/accelerator code for maximum utilization and minimal latency.

Write high-performance PyTorch, Triton, and CUDA, dropping to custom operations when needed.

Develop fused kernels and leverage tensor cores and modern hardware features across 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 models.

One way the first 90 could unfold.

Days 1–30 — Immerse & Diagnose: Profile the current training and inference paths and find the biggest performance wins.

Days 30–60 — Ship & Validate: Land a kernel or architecture optimization that measurably improves utilization or latency.

Days 60–90 — Scale & Systemize: Build the monitoring and automation that keeps performance gains from regressing.

Expert-level Triton/CUDA programming and GPU optimization.

Strong PyTorch skills, including kernel development and custom operations.

Proficiency with profiling tools (NVIDIA Nsight, torch profiler, custom tooling).

Deep understanding of transformer architectures and attention mechanisms.

Experience with compilers and exporters (torch.compile, TensorRT, ONNX, XLA).

Experience optimizing inference workloads for latency and throughput.

Triton compiler and kernel fusion techniques.

Knowledge of warp-level intrinsics and advanced CUDA optimization.

About Luma: Luma's mission is to build unified general intelligence that can generate, understand, and operate in the physical world. We believe multimodality is critical for intelligence — the next step beyond language models comes from vision. Luma is an equal opportunity employer.