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Tilde Research
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Tilde Research
Applications Closed
Posted 9 months ago
Internship
Palo Alto, California
In Person
Smart Summary
As a ML Researcher, you will develop innovative techniques to understand large AI models and improve them. You will collaborate with a team of researchers and engineers to advance interpretability for better performance and control.
We're looking for researchers with experience in deep learning or related areas, with deep technical expertise in subfields of modern ML such as architecture, optimizers, and learning dynamics. Strong communication skills and the ability to come up with and evaluate research ideas are essential.
Must Have Skills for ATS
Deep Learning
Machine Learning
ML
AI
Model Architectures
Optimizers
Reinforcement Learning
RL
Learning Dynamics
Experimentation
Publication Record
Technical Blog Posts
Communication Skills
Research
Pre-training
Post-training
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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