Research Engineer, Anthropic

• When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?"
• 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 some experience contributing to empirical AI research projects
• Have a strong ability to prioritize and direct effort toward the most impactful work and are comfortable operating with ambiguity and questioning assumptions
• Prefer fast-moving collaborative projects to extensive solo efforts
• 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
• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience
• 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

Desirable

• 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
• Some useful analogies might be to think of us as trying to do "biology" or "neuroscience" of neural networks using “microscopes” we build, or as treating neural networks as binary computer programs we're trying to "reverse engineer"
• We often collaborate with teams across Anthropic, such as Alignment Science and Societal Impacts to use our work to make Anthropic’s models safer. We also have an Interpretability Architectures project that involves collaborating with Pretraining
• 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
• 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