Pre-training Data Infrastructure Engineer

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

Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Staff level Engineer to join our Pre-training team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.

Responsibilities

Design and implement data processing infrastructure for large language model training (highly performant, reproducible, traceable)

Develop and maintain core processing primitives (e.g., tokenization, deduplication, chunking) with a focus on scalability

Build robust systems for data quality assurance and validation at scale

Collaborate with research teams to implement novel data processing architectures

Build and operate end-to-end data pipelines that turn raw web-scale corpora into training-ready datasets

You may be a good fit if you have:

5+ YOE outside of internships

Strong software engineering skills with experience building high-throughput fault-tolerant distributed systems

Hands-on experience with distributed computing frameworks, particularly Apache Spark

Excellent problem-solving skills and attention to detail

Strong communication skills and ability to work in a collaborative environment

Advanced degree in Computer Science or related field

Experience with language model training infrastructure

Background in Data Infrastructure, MLOps, or ML infrastructure

Strong candidates may have:

Have significant experience building high-throughput fault-tolerant distributed systems

Expertise with Python and Rust

Passionate about system reliability and performance

Are comfortable working with ambiguous requirements and evolving specifications

Take ownership of problems and drive solutions independently

Are excited about contributing to the development of safe and ethical AI systems

Can balance technical excellence with practical delivery

Are eager to learn about machine learning research and its infrastructure requirements

Sample Projects

Designing and implementing distributed computing architecture for web-scale data processing

Building scalable infrastructure for model training data preparation

Developing fault-tolerant distributed processing systems

Implementing new infrastructure components based on research requirements

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:

$500,000 — $850,000 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 greatly value communication skills.

The easiest way to understand our research directi