Research Scientist / Engineer – Performance Optimization
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 2d ago
The date the source published, not the day we noticed it (2026-10-08). Last seen at its source 1h ago.
We have tracked this listing since 8 Oct 2026 (2 days). The employer's own board has carried it every time we have read it, most recently 1 hour ago.
Is it remote?
Remote, EU
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
Europe
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $133.1k–207.2k/yr
Middle 50% of 148 listings that do state pay — Engineering · all levels · Europe · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Ashby employer's own board first seen 2d ago · last seen 1h ago
The listing
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.