Kernel Engineer (Internship and Full-time)

Tilde Research; Palo Alto (CA); On-site; USD 150,000 - 260,000; Full time

Job summary

Tilde Research is seeking a Kernel Engineer in Palo Alto to design, implement, and optimize high-performance GPU kernels for training and inference workloads. You will collaborate with ML researchers and engineers to push hardware limits, improve throughput, and reduce latency across models and infrastructure.

This role emphasizes performance-aware software, scalable kernels, and hands-on experimentation with CUDA, PyTorch, and Triton.

Qualifications

• Experience in deep learning or related research areas.
• Exceptional capability in ML kernel work (open source contributions, blogs, or similar).
• Familiarity with PyTorch, Triton/TK/TileLang (any 2+), CUDA, and GPU architecture.

Responsibilities

• Design, implement, and optimize GPU kernels for core model operations.
• Collaborate with ML researchers to prototype and scale model architectures.
• Contribute to system-wide efforts to improve efficiency and throughput beyond kernel-level work.

Skills

Deep learning

ML kernel development

PyTorch

CUDA familiarity

GPU architecture knowledge

Strong written communication

Quick learner

Problem solving

Tools

CUDA toolkit

Triton

TileLang

TK

Open source tooling

Job description

Tilde 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