Role description
TildeTilde Research is a moonshot AI lab advancing mechanistic interpretability, new architectures, and pretraining science. We build foundational understanding of models to advance the frontier of intelligence.
About the role:
As a Kernel Engineer at Tilde, you'll design, implement, and optimize high-performance GPU kernels that are critical to scaling our training and inference workloads. Your work will enable faster iteration cycles, higher throughput, and lower latency. You'll work closely with ML researchers and engineers to co-design models and infrastructure that are deeply performance-aware, and help push the limits of what current hardware can support.
What you might work on:
Design, develop, and tune custom GPU kernels for core model operations
Work with ML engineers to prototype and scale novel model architectures
Contribute to system-wide efforts to improve efficiency and throughput, beyond just kernel-level optimizations
You're a good fit if you:
Have experience in deep learning or related research areas
Have demonstrated exceptional capability in working on ML kernels. This can include:
Strong open source contributions
Thoughtful technical blog posts/work logs
Previous experience working on hardware-aligned algorithms
Deep familiarity with PyTorch, Triton/TK/TileLang (>1 of), basic familiarity with CUDA, and knowledge of GPU architecture.
Communicate clearly and effectively, both verbally and in writing
Strong algorithmic thinker
Are able to learn quickly