About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?"

The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe. We’re looking for researchers and engineers to join our efforts.

People mean many different things by "interpretability". We're focused on mechanistic interpretability, which aims to discover how neural network parameters map to meaningful algorithms. If you're unfamiliar with this type of research, you might be interested in this introductory essay, or Zoom In: An Introduction to Circuits. (For a broader overview of work in this space, one of our team's alumni maintains a helpful reading list.)

Some useful analogies might be to think of us as trying to do "biology" or "neuroscience" of neural networks, or as treating neural networks as binary computer programs we're trying to "reverse engineer".

Some of our team's notable publications include A Mathematical Framework for Transformer Circuits, In-context Learning and Induction Heads, and Toy Models of Superposition. This work builds on ideas from members' work prior to Anthropic such as the original circuits thread, Multimodal Neurons, Activation Atlases, and Building Blocks.

We aim to create a solid foundation for mechanistically understanding neural networks and making them safe (see our recent vision post). In the short term, this means a we focus a lot of our attention on the issue of "superposition" (see Toy Models of Superposition, Superposition, Memorization, and Double Descent, and our May 2023 update). But this is just a stepping stone towards our goal of mechanistically understanding neural networks.

We often collaborate with teams across Anthropic, such as Alignment Science and Societal Impacts. We also have an Interpretability Architectures project that involves collaborating with Pretraining. If you would be especially excited to work on a project that touches upon the intersection of Interpretability and another team, feel free to note down the specific team(s) you’d be interested in collaborating with.

Responsibilities:

Develop methods for understanding LLMs by reverse engineering algorithms learned in their weights
Design and run robust experiments, both quickly in toy scenarios and at scale in large models
Build infrastructure for running experiments and visualizing results
Work with colleagues to communicate results internally and publicly

You may be a good fit if you:

Have a strong track record of scientific research (in any field), and have done some work on Interpretability
Enjoy team science – working collaboratively to make big discoveries
Are comfortable with messy experimental science. We're inventing the field as we work, and the first textbook is years away
You view research and engineering as two sides of the same coin. Every team member writes code, designs and runs experiments, and interprets results
You can clearly articulate and discuss the motivations behind your work, and teach us about what you've learned. You like writing up and communicating your results, even when they're null

Strong candidates may also have experience with:

High performance, large-scale ML systems
GPUs, Kubernetes, Pytorch, or OS internals
Language modeling with transformers
Reinforcement learning
Large-scale ETL

Representative Projects:

Garcon - a tool which allows researchers to easily access LLMs internals from a jupyter notebook
ETL pipelines for collecting and analyzing LLM activations at large scale
Profiling and Optimizing ML Training, including parallelizing to many GPUs
Make launching ML experiments and manipulating+analyzing the results fast and easy
Writing a design doc for fault tolerance strategies
Creating an interactive visualization of attention between tokens in a language model

Familiarity with Python is required for this role.

The expected salary range for this position is:

Annual Salary:

£230,000—£455,000 GBP

Logistics

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

US visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate; operations roles are especially difficult to support. But if we make you an offer, we will make every effort to get you into the United States, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Compensation and Benefits*

Anthropic’s compensation package consists of three elements: salary, equity, and benefits. We are committed to pay fairness and aim for these three elements collectively to be highly competitive with market rates.

Equity - On top of this position's salary (listed above), equity will be a major component of the total compensation. We aim to offer higher-than-average equity compensation for a company of our size, and communicate equity amounts at the time of offer issuance.

US Benefits - The following benefits are for our US-based employees:

Optional equity donation matching at a 3:1 ratio, up to 50% of your equity grant.
Comprehensive health, dental, and vision insurance for you and all your dependents.
401(k) plan with 4% matching.
22 weeks of paid parental leave.
Unlimited PTO – most staff take between 4-6 weeks each year, sometimes more!
Stipends for education, home office improvements, commuting, and wellness.
Fertility benefits via Carrot.
Daily lunches and snacks in our office.
Relocation support for those moving to the Bay Area.

UK Benefits - The following benefits are for our UK-based employees:

Optional equity donation matching at a 3:1 ratio, up to 50% of your equity grant.
Private health, dental, and vision insurance for you and your dependents.
Pension contribution (matching 4% of your salary).
21 weeks of paid parental leave.
Unlimited PTO – most staff take between 4-6 weeks each year, sometimes more!
Health cash plan.
Life insurance and income protection.
Daily lunches and snacks in our office.

* This compensation and benefits information is based on Anthropic’s good faith estimate for this position as of the date of publication and may be modified in the future. Employees based outside of the UK or US will receive a different benefits package. The level of pay within the range will depend on a variety of job-related factors, including where you place on our internal performance ladders, which is based on factors including past work experience, relevant education, and performance on our interviews or in a work trial.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both as needed.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation based in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

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Research Engineer, Interpretability