Research Engineer / Research Scientist, Tokens

AnthropicSan Francisco, CA
90d$340,000 - $425,000

About The Position

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. Note: This is an "evergreen" role that we keep open on an ongoing basis. We receive many applications for this position, and you may not hear back from us directly if we do not currently have an open role on any of our teams that matches your skills and experience. We encourage you to apply despite this, as we are continually evaluating for top talent to join our team. You are also welcome to reapply as you gain more experience, but we suggest only reapplying once per year. We may also put up separate, team-specific job postings. In those cases, the teams will give preference to candidates who apply to the team-specific postings, so if you are interested in a specific team please make sure to check for team-specific job postings!

Requirements

  • Significant software engineering experience.
  • Results-oriented, with a bias towards flexibility and impact.
  • Ability to pick up slack, even if it goes outside your job description.
  • Enjoy pair programming.
  • Desire to learn more about machine learning research.
  • Care about the societal impacts of your work.

Nice To Haves

  • Experience with high performance, large-scale ML systems.
  • Familiarity with GPUs, Kubernetes, Pytorch, or OS internals.
  • Experience with language modeling with transformers.
  • Knowledge of reinforcement learning.
  • Experience with large-scale ETL.

Responsibilities

  • Build large scale ML systems from the ground up.
  • Make the cluster more reliable for big jobs.
  • Improve throughput and efficiency.
  • Run and design scientific experiments.
  • Improve dev tooling.

Benefits

  • Competitive compensation and benefits.
  • Optional equity donation matching.
  • Generous vacation and parental leave.
  • Flexible working hours.
  • Lovely office space for collaboration.
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