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Tilde Research

ML Researcher (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
18+ days
First seen
Oct 18, 2026
Apply on Tilde Research's site (opens in a new tab)

Posting record

No flags

Open 18+ 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 Researcher, you will develop innovative techniques to deeply understand how large AI models work—and use those insights to make them better. You'll work on training, analyzing, and evaluating cutting-edge models, collaborating closely with a team of researchers and engineers to advance interpretability as a tool for improving performance and control.

What you might work on

  • Designing, prototyping, and optimizing novel model architectures
  • Investigating why models or specific components behave the way they do—and how to improve them
  • Curating targeted datasets to elicit and instill specific behaviors or capabilities in models
  • Collaborating on papers, blog posts, and open-source tools to share insights with the broader community

You're a good fit if you

  • Have experience in deep learning or related research areas
  • Have deep technical expertise in some subfield(s) of modern ML, e.g. architecture, optimizers, RL, learning dynamics, etc.. This can include through:
  • Strong publication record
  • Thoughtful technical blog posts
  • Have experience working with large-scale pre/post-training infrastructure for experimentation
  • Communicate clearly and effectively, both verbally and in writing
  • Can come up with and evaluate research ideas
  • Are able to learn quickly

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