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Research Engineer

San Francisco, California, USA

Full time



About the Role

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.

You might 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 machine learning research

  • Care about the societal impacts of your work

Strong candidates may also have experience with some of the following

  • High performance, large-scale ML systems

  • GPUs, kubernetes, pytorch, OS internals

  • Language modeling with transformers

  • Reinforcement learning

  • Large-scale ETL

Representative projects

  • Optimizing the throughput of a new attention mechanism

  • Comparing the compute efficiency of two Transformer variants

  • Making a Wikipedia dataset in a format models can easily consume

  • Scaling a distributed training job to thousands of GPUs

  • Writing a design doc for fault tolerance strategies

  • Creating an interactive visualization of attention between tokens in a language model

How we're different

  • We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles.

  • We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're most excited to hire researchers from diverse backgrounds who share this perspective.

  • We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

  • We're trying to build a core of knowledge and intuition about the most robustly effective innovations in AI, and so thoroughly-documented null results are almost as valuable as positive discoveries.

  • We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both as needed.

Compensation and Benefits*

Anthropic’s compensation package consists of three elements: salary, equity, and benefits. We are committed to pay fairness and aim for these three elements collectively to be highly competitive with market rates.

Salary - The expected salary range for this position is $250k - $445k.

Equity -  Equity will be a major component of the total compensation for this position. We aim to offer higher-than-average equity compensation for a company of our size, and communicate equity amounts at the time of offer issuance.

Benefits - Benefits we offer include:

- Optional equity donation matching at a 3:1 ratio, up to 50% of your equity grant.

- Comprehensive health, dental, and vision insurance for you and all your dependents.

- 401(k) plan with 4% matching.

- 21 weeks of paid parental leave.

- Unlimited PTO – most staff take between 4-6 weeks each year, sometimes more!

- Stipends for education, home office improvements, commuting, and wellness.

- Fertility benefits via Carrot.

- Daily lunches and snacks in our office.

- Relocation support for those moving to the Bay Area.

* This compensation and benefits information is based on Anthropic’s good faith estimate for this position, in San Francisco, CA, as of the date of publication and may be modified in the future. The level of pay within the range will depend on a variety of job-related factors, including where you place on our internal performance ladders, which is based on factors including past work experience, relevant education, and performance on our interviews or in a work trial.

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