anthropic
Research Engineer / Scientist, Alignment
At a Glance
- Location
- United States
- Posted
- 2026-02-19T10:21:06-05:00
Key Requirements
Domain Knowledge
- Education
Requirements
Formal certifications or education credentials
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.
Compensation & Benefits
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:
$350,000
—
$500,000 USD
Logistics
Responsibilities
You want to build and run elegant and thorough machine learning experiments to help us understand and steer the behavior of powerful AI systems.
You care about making AI helpful, honest, and harmless, and are interested in the ways that this could be challenging in the context of human-level capabilities.
As a Research Engineer on Alignment Science, you'll contribute to exploratory experimental research on AI safety, with a focus on risks from powerful future systems (like those we would designate as ASL-3 or ASL-4 under our
Responsible Scaling Policy
), often in collaboration with other teams including Interpretability, Fine-Tuning, and the Frontier Red Team.
provides an overview of topics that the Alignment Science team is either currently exploring or has previously explored.
About the Company
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.
Strong candidates may also:
Have experience authoring research papers in machine learning, NLP, or AI safety
Have experience with LLMs
Have experience with reinforcement learning