Job at Luma AI
Research Scientist / Engineer – Performance Optimization
- Employer
- Luma AI
- Location
- London, UK · New York, NY · Redwood City, CA · Remote, EU · Remote, US · Seattle, WA
- Workplace
- hybrid
- Employment
- full-time
- Department
- Research & AI
- Team
- Inference Compute
- Salary
- $195,000 to $395,000 per year, as the employer lists it
- Last published
- First listed on USASI
Apply on Luma AI’s site (external site)View the posting on Luma AI’s job board (external site)
This posting comes from Luma AI’s own Ashby job board. USASI checks that board every day and removes this page once the employer closes the posting. The description below is Luma AI’s text as published; USASI does not edit it, does not recruit, and does not take applications. How jobs are collected.
Job description
You'll make Luma's multimodal models fast — profiling and optimizing GPU, CPU, and accelerator code so they train efficiently and deploy at scale without sacrificing quality. You'll write the kernels and operations that get the most out of the hardware.
This is deep performance work: fused kernels, tensor cores, Triton and CUDA, distributed multi-node deployment. It fits someone with expert GPU-optimization skills and a deep understanding of transformer internals. If you're not at home in CUDA, Triton, and profilers, this is the wrong depth.
What You'll Own
- 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.
First 90 Days
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.
What You Bring
- 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.
Nice to Have
- 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.
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