hirly

Luma

Research Scientist / Engineer – Performance Optimization

Redwood City, CA

See how you match this job — and similar ones. Free.

Upload your resume and hirly scores it against this role at Luma first, then against similar open jobs, and shows where you fit and why.

PDF or DOCX, up to 12MB. No sign-up to see your matches.

Get past the screening software and onto a recruiter's desk

hirly rewrites your resume for this job — matching the keywords and skills in the posting, moving your most relevant experience to the top, and writing a cover letter to fit. About 30 seconds.

  • Keywords matched to this posting
  • Fit score before you apply
  • Cover letter included

Matched against 2.3M live jobs from 200,000+ employers in 200+ countries.

Tailor my resume for this job →

hirly's read of this role

Role family
Data & ML
Seniority
Mid level
Country
US
Work mode
On-site / unstated
First seen by hirly
28 Sept 2026

Derived automatically from the posting. Upload your resume above to see how the role scores against it.

the posting

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.

Original posting on Luma's site ↗

Want this one?

Upload your resume and hirly rewrites it for this job and writes the cover letter — in about thirty seconds, before you sign up.

Tailor my resume for this job