# [Hiring] Principia Research Fellows

**Principia · London · Fixed-term (6 months) with potential extension · Starting ASAP**

We are launching **Principia**, a new technical research agenda led by **Andrew Saxe** focused on **theoretical models of representation learning and generalization** in modern machine learning systems.

We are hiring **2–3 research fellows** to join this effort. We are **based in London**and offer an initial **6-month fixed-term contract starting ASAP, with the possibility of extension based** on mutual interest and funding.

**About Principia**
-------------------

Principia’s mission is to develop **foundational, predictive theory for modern machine learning systems** that can help us understand complex network behaviors, including those critical for **AI safety and alignment**. We study **simple, analytically tractable “model organisms”** that capture essential learning dynamics and failure modes, with the goal of explaining and anticipating behaviors observed in large neural networks.

**About the research agenda**
-----------------------------

Our agenda aims to develop **analytically and empirically tractable toy models** that explain how representations emerge, specialize, and generalize under different training regimes. The broader goal is to build a theory that is:

*   mathematically clean and interpretable,
*   empirically connected to phenomena observed in modern neural networks, and
*   capable of generating **testable predictions relevant to AI safety**.  
     

Research directions may include learning dynamics, inductive biases, multi-stage training, and generalization phase transitions in simplified model systems.

Our projects will build on prior and ongoing work, including (but not limited to):

*   **Gated Linear Neural Networks \[**[**Link**](https://arxiv.org/abs/2207.10430)**\]**
*   **Linear Attention \[**[**Link**](https://arxiv.org/abs/2501.16265)**\]**
*   **RL Perceptron–style models \[**[**Link**](https://journals.aps.org/prx/abstract/10.1103/PhysRevX.15.021051)**\]**  
     

Fellows will work closely with Andrew Saxe, fellow Principia researchers, and collaborators through regular technical discussions, joint problem formulation, and collaborative research projects.

**About the role**
------------------

As a research fellow at Principia, you will contribute directly to this agenda through both independent and collaborative work. This may include:

*   developing formal or simplified models,
*   conducting theoretical and analytical work,
*   designing and performing empirical experiments to validate theoretical predictions.

The role is research-focused, emphasizing depth, clarity, and conceptual progress.

**Who we’re looking for**
-------------------------

We are particularly excited about candidates who:

*   Have **demonstrated research experience** in a relevant area
*   Have a **strong theoretical background**, for example, in:
    *   theoretical machine learning
    *   physics (especially statistical physics, dynamical systems, or related areas)
    *   applied mathematics or related fields
*   Are comfortable working with **mathematical models and analytical arguments**

Formal degrees (including a PhD) are **preferred but not required**. What matters most is evidence of research ability — for example, through publications, preprints, technical reports, or substantial independent research projects.

Experience with **empirical or computational work** (e.g,. PyTorch, JAX, numerical experiments) is a plus.

**Working style and environment**
---------------------------------

*   **Research hub in London (LISA)**, with close collaboration and frequent technical discussion with existing collaborators and researchers
*   Emphasis on **depth, clarity, and conceptual progress**, rather than rapid productization
*   Support for conference travel and internal research retreats
*   Providing dedicated cloud compute credits to support empirical experiments 
*   Well-suited for researchers seeking focused time to develop **foundational theory relevant to AI safety**

**Duration, salary, and visas**
-------------------------------

*   **Contract Term**  
    This is initially a **6-month fixed-term** position, **starting ASAP**. We are actively raising additional funding to support longer-term appointments. For exceptional candidates, we may offer a 12-month term from the outset if required. Extensions will be considered depending on funding availability and mutual interest.
*   **Compensation**  
    Salary range of **USD 40,000–50,000 for a 6-month contract**, depending on experience and seniority.
*   **Visa**  
    While we are unable to offer visa sponsorship in this hiring round, we welcome international applicants and will actively support candidates through the visa process if needed. 

**How to apply**
----------------

Please fill in [the application form](https://forms.gle/xKukH74iX16pPGrA6) by **25 February 2026 at 11:59 PM GMT,** which includes

*   a CV,
*   a brief statement of interest (500 words max) describing your background, research experience, and interest in this agenda, and
*   contact details for 2-3 referees
*   **Optional:** Supporting materials that reflect your research ability (e.g. preprints, code repositories, technical writing if not included in CV already)

We are reviewing applications **on a rolling basis**and may make offers potentially before the application deadline. **We encourage applicants to submit the form as early as possible. **

We will contact selected applicants, ideally within a week of the application, to schedule an interview consisting of a brief research talk and 1:1s.   
 

**Diversity, Equality and Inclusion**
-------------------------------------

We aim to promote equal opportunities and eliminate discrimination. We welcome applications from all backgrounds, regardless of gender, ethnicity, religion, or disability, and are happy to discuss any reasonable adjustments you may require throughout the hiring process.

**Questions**
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For any questions, please leave a comment or use this [form](https://forms.gle/mBabAr1fFkfeYjN87).