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Research Scientist

2 months ago

West Yorkshire, United Kingdom Anthropic Full time
About the Role:

We're seeking a talented researcher to join our Interpretability team at Anthropic. As a key member of our team, you'll be responsible for implementing and analyzing research experiments, setting up and optimizing research workflows, and building tools and abstractions to support rapid pace of research experimentation.

Our team's mission is to reverse engineer how trained models work, with a focus on mechanistic interpretability. We believe that a mechanistic understanding is the most robust way to make advanced systems safe. If you're passionate about machine learning research and its applications, and want to collaborate closely with researchers, this could be the perfect opportunity for you.

We're looking for someone with 5-10+ years of experience building software, who is highly proficient in at least one programming language and productive with Python. You should have a strong ability to prioritize and direct effort toward the most impactful work, and be comfortable operating with ambiguity and questioning assumptions.

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 a strong background in machine learning research and its applications
  • You're highly proficient in at least one programming language and productive with Python
  • You have a strong ability to prioritize and direct effort toward the most impactful work
  • You're comfortable operating with ambiguity and questioning assumptions
  • You 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 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