Luma
Copy of Research Scientist / Engineer – Performance Optimization

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You'll make Luma's multimodal models fast
You'll profile and optimize 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.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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.


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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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