TLDR: We’re hiring two research assistants to work on advancing developmental interpretability and other applications of singular learning theory to alignment.

About Us

Timaeus’s mission is to empower humanity by making breakthrough scientific progress on alignment. Our research focuses on applications of singular learning theory to foundational problems within alignment, such as interpretability (via “developmental interpretability”), out-of-distribution generalization (via “structural generalization”), and inductive biases (via “geometry of program synthesis”). Our team spans Melbourne, the Bay Area, London, and Amsterdam, collaborating remotely to tackle some of the most pressing challenges in AI safety.

For more information on our research and the position, see our Manifund application, this update from a few months ago, our previous hiring call and this advice for applicants.

Position Details

Title: Research Assistant
Location: Remote
Duration: 6-month contract with potential for extension.
Compensation: Starting at $35 USD per hour as a contractor (no benefits).
Start Date: Starting as soon as possible.

Key Responsibilities

Conduct experiments using PyTorch/JAX on language models ranging from small toy systems to billion-parameter models.
Collaborate closely with a team of 2-4 researchers.
Document and present research findings.
Contribute to research papers, reports, and presentations.
Maintain detailed research logs.
Assist with the development and maintenance of codebases and repositories.

Projects

As a research assistant, you would likely work on one of the following two projects/research directions (this is subject to change):

Devinterp of language models: (1) Continue scaling up techniques like local learning coefficient (LLC) estimation to larger models to study the development of LLMs in the 1-10B range. (2) Work on validating the next generations of SLT-derived techniques such as restricted LLC estimation and certain kinds of weight- and data-correlational analysis. This builds towards a suite of SLT-derived tools for automatically identifying and analyzing structure in neural networks.
SLT of safety fine-tuning: Investigate the use of restricted LLCs as a tool for measuring (1) reversibility of safety fine-tuning and (2) susceptibility to jailbreaking. Having now validated many of our predictions around SLT, we are

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Loiruck Godwin

Loiruck Godwin

Loiruck Godwin

Loiruck Godwin