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Tilde Research

ML 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.

How well do you match?

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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 ML Engineer, you’ll build and operate the infrastructure that makes cutting-edge machine learning research possible. At Tilde, we believe meaningful progress in AI requires not just novel ideas, but the ability to rapidly test, scale, and iterate on them—and that demands exceptional engineering.

You’ll work on the systems that support training and evaluating large models, scaling experimental pipelines, and building the infrastructure necessary to actually understand models. Your work will be foundational to our research, making it possible to explore ambitious ideas that push the boundaries of performance, interpretability, and control.

What you might work on

  • Optimize inference and training throughput for novel model architectures
  • Build and maintain high-performance distributed training infrastructure
  • Collaborate with researchers to translate insights into measurable improvements in model performance and understanding

You're a good fit if you

  • Have experience in deep learning or related research areas
  • Have demonstrated exceptional capability in working on ML infrastructure. This can include:
  • Strong open source contributions
  • Thoughtful technical blog posts/work logs
  • Previous experience working with large-scale pre/post-training infrastructure
  • Deep familarity with Pytorch or Jax, basic familiarity Triton/Tilelang/TK etc.
  • Communicate clearly and effectively, both verbally and in writing
  • Can design and orchestrate end-to-end ML pipelines
  • Are able to learn quickly

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