Research Engineer, Interpretability

The Interpretability team at Anthropic is working to reverse-engineer how 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. Few things can accelerate this progress more than great infrastructure. As a research engineer, you will build and maintain infrastructure used by the whole team, including yourself. You'll touch all parts of our code and infrastructure, whether that's making the cluster more reliable for our big jobs, improving throughput and efficiency, running and designing scientific experiments, or improving our dev tooling. You’re motivated to understand our research so you can write code that accelerates it. 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 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. About Anthropic Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our customers and for society as a whole. Our interdisciplinary team has experience across ML, physics, policy, business and product. Responsibilities: - Build infrastructure for running experiments and visualizing results - Design and run robust experiments, both quickly in toy scenarios and at scale in large models - Develop methods for understanding LLMs by reverse engineering algorithms learned in their weights - Work with colleagues to communicate results internally and publicly You may be a good fit if you: - Have significant software engineering experience - Are results-oriented, with a bias towards flexibility and impact - Pick up slack, even if it goes outside your job description - Enjoy pair programming (we love to pair!) - Want to learn more about interpretability research - Care about the societal impacts of your work 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. Annual Salary: - The expected salary range for this position is $280k - $520k USD. Logistics Location-based hybrid policy: Currently, we expect all staff to be in our office at least 25% of the time. Deadline to apply: We expect to be hiring for this role intermittently, but our plan is to hire a few people in the next 2-3 months, and then will likely slow hiring while the team settles. (This opportunity was originally posted early July.) 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 im