Data Engineer, Safeguards London, UK

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 looking for a Data Engineer to join the Safeguards team and build the data foundations that keep our AI systems safe. The Safeguards team works to monitor models, prevent misuse, and ensure user well‑being — and doing that well requires robust, reliable data infrastructure. In this role, you'll design and build the data pipelines, warehousing solutions, and analytical tooling that power our safety and trust efforts at scale. You'll work closely with engineers, data scientists, and policy teams to ensure the Safeguards organization has the data it needs to detect abuse patterns, measure the effectiveness of safety interventions, and make informed decisions about model behavior and enforcement. This is a high‑impact role where your work will directly support Anthropic's mission to develop AI that is safe and beneficial.

Responsibilities

• Design, build, and maintain scalable data pipelines that support safety monitoring, abuse detection, and enforcement workflows.
• Develop and optimize data models and warehousing solutions to enable efficient analysis of large-scale usage and safety data.
• Build and maintain dashboards and reporting infrastructure that give Safeguards teams visibility into model behavior, misuse patterns, and enforcement outcomes.
• Collaborate with engineers to integrate data from multiple sources—including model outputs, user reports, and automated classifiers—into a unified analytical layer.
• Implement data quality frameworks, monitoring, and alerting to ensure the reliability of safety‑critical data.
• Partner with research teams to surface data insights that inform model improvements and safety interventions.
• Develop self‑service data tooling that enables stakeholders to explore safety data and generate reports independently.
• Contribute to data governance practices, including access controls, retention policies, and privacy‑compliant data handling.

You may be a good fit if you:

• Have 3+ years of experience in data engineering, analytics engineering, or a related role.
• Are proficient in SQL and Python, with experience building and maintaining ETL/ELT pipelines.
• Have hands‑on experience with modern data stack tools such as dbt, Airflow, Spark, or similar orchestration and transformation frameworks.
• Have worked with cloud data platforms (BigQuery, Redshift, Snowflake, or similar).
• Are comfortable building dashboards and data visualizations using tools like Looker, Tableau, or Metabase.
• Communicate clearly and can translate complex data concepts for both technical and non‑technical audiences.
• Are results‑oriented, flexible, and willing to pick up slack even when it falls outside your job description.
• Care about the societal impacts of AI and are motivated by safety work.

Strong candidates may have:

• Experience with trust & safety, integrity, fraud, or abuse detection data systems.
• Experience with large‑scale event streaming systems (Kafka, Pub/Sub, Kinesis).
• Built data infrastructure that supports ML model monitoring or evaluation.
• Familiarity with data privacy and compliance frameworks (GDPR, CCPA, or similar).
• A background in statistical analysis, or experience collaborating closely with data scientists.
• Developed internal tooling or self‑service analytics platforms.

Strong candidates need not have:

• A formal degree in Computer Science or a related field — we value practical experience and demonstrated ability over credentials.
• Prior experience in AI or machine learning — you'll learn the domain‑specific context on the job.
• Previous experience at an AI safety or research organization.
• Deep expertise across every tool listed above — familiarity with a subset and a willingness to learn is enough.

Logistics

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.

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 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! Anthropic is a public benefit corporation headquartered in San Francisco. 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.