Research Scientist / Engineer – Performance Optimization

AItoolnavio; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Luma is seeking an expert GPU optimization engineer to accelerate multimodal transformer workloads. You will profile, tune, and deploy kernel- and operator-level improvements across GPUs, CPUs, and accelerators to maximize training throughput and minimize latency.

You will implement fused kernels, leverage tensor cores, and push Triton/CUDA capabilities, while collaborating on distributed multi-node deployment and performance instrumentation.

Qualifications

• 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.

Responsibilities

• 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.
• Scale and systemize performance improvements for long-term reliability.

Skills

Triton/CUDA programming

PyTorch kernel development

GPU optimization

Profiling tools

Tools

NVIDIA Nsight

Torch profiler

Custom tooling

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.