ML Infrastructure Engineer, Interpretability

Key Skills

GPUs, Infrastructure as code, Java Programming, Large Language Models - LLMs, Machine learning techniques, Optimization, Python Programming, PyTorch, Rust

Job Description

About the role:

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’s mission is to reverse engineer how trained models work. 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.

We're focused on mechanistic interpretability, which aims to discover how neural network parameters map to meaningful algorithms. 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.

We recently showed that we could extract millions of meaningful features from Anthropic’s production Claude 3.0 Sonnet model, along with an initial demonstration of how we can use these features to change the model’s behavior by creating “Golden Gate Claude”. Achieving these results required a large engineering effort including optimizing sparse autoencoders (SAEs) across many GPUs, and building tools to visualize millions of features. Work like this is central to our roadmap of using mechanistic interpretability to improve the safety of LLMs like Claude.

A few places to learn more about our work and team are this introduction to Interpretability from our research lead, Chris Olah; a discussion of our work on the Hard Fork podcast produced by the New York Times, and this blog post (and accompanying video) sharing more about some of the engineering challenges we’d had to solve to get these results.

We collaborate with teams across Anthropic, such as Alignment Science and Societal Impacts to use our work to make Anthropic’s models safer.

Responsibilities:

• Implement and analyze research experiments, both quickly in toy scenarios and at scale in large models
• Set up and optimize research workflows to run efficiently and reliably at large scale
• Build tools and abstractions to support rapid pace of research experimentation
• Develop and improve tools and infrastructure to support other teams in using Interpretability’s work to improve model safety

You may be a good fit if you:

• Have 5-10+ years of experience building software
• Are highly proficient in at least one programming language (e.g., Python, Rust, Go, Java) and productive with python
• Have a strong ability to prioritize and direct effort toward the most impactful work and are comfortable operating with ambiguity and questioning assumptions.
• Want to learn more about machine learning research and its applications and collaborate closely with researchers
• Care about the societal impacts and ethics of your work

Strong candidates may also have experience with:

• Designing a code base so that anyone can quickly code experiments, launch them, and analyze their results without hitting bugs
• Optimizing the performance of large-scale distributed systems
• Collaborating closely with researchers, ML engineers, or data scientists
• Language modeling with transformers
• GPUs or Pytorch

Representative Projects:

• Building Garcon, a tool that allows researchers to easily access LLMs internals from a jupyter notebook
• Setting up and optimizing a pipeline to efficiently collect petabytes of transformer activations and shuffle them.
• Profiling and optimizing ML training, including parallelizing to many GPUs
• Make launching ML experiments and manipulating+analyzing the results fast and easy
• Creating an interactive visualization of attention between tokens in a language model

The expected salary range for this position is:

Annual Salary:

$280,000—$625,000 USD

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.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, 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 - For eligible roles, 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.
• 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.
• 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.