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Tilde Research

Kernel Engineer (Internship and Full-time)

  • Seen live on Tilde Research's site 6 days ago
  • Intern / co-op
Pay
Not stated
Location
San Francisco
Open for
17+ days
First seen
Oct 18, 2026
Apply on Tilde Research's site (opens in a new tab)

Posting record

No flags

Open 17+ days

The posting's record, oldest first
  1. Tilde Research's site dates the posting
  2. Ghostless first sees this listing
  3. Last checked: still up
How we know

It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.

Ghostless records the day it first sees a posting, so a repost can't reset its age. This one was already up when Ghostless started tracking Tilde Research, so its true age is longer.

Last checked Oct 29, 2026.

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What the posting discloses

Not stated: Pay, real vacancy, AI screening.

Why these three

Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.

Some employers add pay only on their own site. Check the original posting before you rule it out.

Skills mentioned

About the role

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

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