Research Engineer / Research Scientist, Tokens at Anthropic

Hybrid - New York City, NY; Seattle, WA; San Francisco, CA

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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. You want to build large scale ML systems from the ground up. You care about making safe, steerable, trustworthy systems. As a Research Engineer, 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 excited to write code when you understand the research context and more broadly why it’s important.

Salary

USD 350,000 - 500,000

Requirements

Skills

  • 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 machine learning research
  • Care about the societal impacts of your work
  • High performance, large-scale ML systems
  • GPUs, Kubernetes, Pytorch, or OS internals
  • Language modeling with transformers
  • Reinforcement learning
  • Large-scale ETL
  • Bachelor’s degree or an equivalent combination of education, training, and/or experience
  • A field relevant to the role as demonstrated through coursework, training, or professional experience

Responsibilities

  • Build large‑scale ML systems from the ground up
  • Improve cluster reliability for big jobs
  • Improve throughput and efficiency
  • Run and design scientific experiments
  • Improve development tooling
  • Write code aligned with research context

Technologies

GPUsKubernetesPyTorchOS internalsTransformersReinforcement learningLarge‑scale ETL

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