Research Engineer / Research Scientist, Tokens

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!

You may be a good fit if you:

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• 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:

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• High performance, large-scale ML systems
• GPUs, Kubernetes, Pytorch, or OS internals
• Language modeling with transformers
• Reinforcement learning
• Large-scale ETL

Representative projects:

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• 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

The expected base compensation for this position is below. Our total compensation package for full-time employees includes equity, benefits, and may include incentive compensation.

Annual Salary:

$340,000 - $425,000 USD

Logistics

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Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.**

Location-based hybrid policy:** Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

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. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

How we're different

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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 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.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

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We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy

for using AI in our application process