Machine Learning Systems Engineer, Research Tools

Job Application for Machine Learning Systems Engineer, Research Tools at Anthropic

Machine Learning Systems Engineer, Research Tools San Francisco, CA | New York City, NY | Seattle, WA

About Anthropic

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.

About the Role:

We are seeking an experienced Machine Learning Systems Engineer to join our Encodings and Tokenization team at Anthropic. This cross-functional role will be instrumental in developing and optimizing the encodings and tokenization systems used throughout our Finetuning workflows. As a bridge between our Pretraining and Finetuning teams, you'll build critical infrastructure that directly impacts how our models learn from and interpret data. Your work will be foundational to Anthropic's research progress, enabling more efficient and effective training of our AI systems while ensuring they remain reliable, interpretable, and steerable.

Responsibilities:

Design, develop, and maintain tokenization systems used across Pretraining and Finetuning workflows

Optimize encoding techniques to improve model training efficiency and performance

Collaborate closely with research teams to understand their evolving needs around data representation

Build infrastructure that enables researchers to experiment with novel tokenization approaches

Implement systems for monitoring and debugging tokenization-related issues in the model training pipeline

Create robust testing frameworks to validate tokenization systems across diverse languages and data types

Identify and address bottlenecks in data processing pipelines related to tokenization

Document systems thoroughly and communicate technical decisions clearly to stakeholders across teams

You May Be a Good Fit If You:

Have significant software engineering experience with demonstrated machine learning expertise

Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments

Can work independently while maintaining strong collaboration with cross-functional teams

Are results-oriented, with a bias towards flexibility and impact

Have experience with machine learning systems, data pipelines, or ML infrastructure

Are proficient in Python and familiar with modern ML development practices

Have strong analytical skills and can evaluate the impact of engineering changes on research outcomes

Pick up slack, even if it goes outside your job description

Enjoy pair programming (we love to pair!)

Care about the societal impacts of your work and are committed to developing AI responsibly

Strong Candidates May Also Have Experience With:

Working with machine learning data processing pipelines

Building or optimizing data encodings for ML applications

Implementing or working with BPE, WordPiece, or other tokenization algorithms

Performance optimization of ML data processing systems

Multi-language tokenization challenges and solutions

Research environments where engineering directly enables scientific progress

Distributed systems and parallel computing for ML workflows

Large language models or other transformer-based architectures (not required)

Deadline to apply: None. Applications will be reviewed on a rolling basis.

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary: $320,000 - $405,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

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.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

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 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…