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ML Researcher (Internship and Full-time)

TilderesearchSan Francisco🇺🇸United StatesPosted Jul 14, 2025

Why This Role Stands Out

Dive into groundbreaking AI research at Tilde Research, where you'll gain invaluable experience developing foundational understanding of cutting-edge models and contributing to the advancement of intelligence. This role is perfect for driven individuals with a deep technical expertise in ML who are eager to collaborate on innovative techniques and publish their findings. Apply now to shape the future of AI research!

Quick Overview

Seniority
Entry Level
Employment type
Full Time
Work mode
On Site
Location
San Francisco, United States
Posted
1 year ago
Deep Learning

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 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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