ML/Research Engineer, Safeguards

About the role

We are looking for ML Engineers and Research Engineers to help detect and mitigate misuse of our AI systems. As a member of the Safeguards ML team, you will build systems that identify harmful use—from individual policy violations to sophisticated, coordinated attacks—and develop defenses that keep our products safe as capabilities advance. You will also work on systems that protect user wellbeing and ensure our models behave appropriately across a wide range of contexts. This work feeds directly into Anthropic's Responsible Scaling Policy commitments.

Responsibilities

• Develop classifiers to detect misuse and anomalous behavior at scale. This includes developing synthetic data pipelines for training classifiers and methods to automatically source representative evaluations to iterate on
• Build systems to monitor for harms that span multiple exchanges, such as coordinated cyber attacks and influence operations, and develop new methods for aggregating and analyzing signals across contexts
• Evaluate and improve the safety of agentic products—developing both threat models and environments to test for agentic risks, and developing and deploying mitigations for prompt injection attacks
• Conduct research on automated red-teaming, adversarial robustness, and other research that helps test for or find misuse

You may be a good fit if you

• Have 4+ years of experience in ML engineering, research engineering, or applied research, in academia or industry
• Have proficiency in Python and experience building ML systems
• Are comfortable working across the research-to-deployment pipeline, from exploratory experiments to production systems
• Are worried about misuse risks of AI systems, and want to work to mitigate them
• Have strong communication skills and ability to explain complex technical concepts to non-technical stakeholders

Strong candidates may also have experience with

• Language modeling and transformers
• Building classifiers, anomaly detection systems, or behavioral ML
• Adversarial machine learning or red-teaming
• Interpretability or probes
• Reinforcement learning
• High-performance, large-scale ML systems