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.
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
- 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
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.
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.
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