Posting record
Open 18+ days
- Tilde Research's site dates the posting
- Ghostless first sees this listing
- 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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Upload your resumeWhat 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.
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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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