ML/Research Engineer, Safeguards

Anthropic; New York (NY); Hybrid; USD 350,000 - 500,000; Full time

Job summary

Anthropic in New York is seeking ML Engineers and Research Engineers to detect and mitigate misuse of AI systems. You will build classifiers and systems to monitor and improve safety. The role requires at least 4 years of relevant experience, proficiency in Python, and strong communication skills. Compensation ranges from $350,000 to $500,000 annually with a hybrid work policy requiring some office presence.

Qualifications

• 4+ years of experience in ML engineering, research engineering, or applied research.
• Comfortable working across the research-to-deployment pipeline.
• Concerned about misuse risks of AI systems.

Responsibilities

• Develop classifiers to detect misuse and anomalous behavior.
• Build systems to monitor for harms across exchanges.
• Evaluate and improve the safety of agentic products.
• Conduct research on automated red-teaming.

Skills

Proficiency in Python

Experience building ML systems

Strong communication skills

Education

Bachelor’s degree or equivalent

Job description

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 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

Requirements

• 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

Preferred Experience

• 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

Compensation

Annual Salary: $350,000—$500,000 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, but we may not be able to sponsor every role and every candidate.

Safe and Inclusive Workplace

We encourage you to apply even if you do not believe you meet every single qualification. We welcome diverse perspectives to help us build better AI systems.