Research Engineer, Production Model Post-Training
About this role
Role Overview
Anthropic is seeking a Research Engineer for Production Model Post-Training to work in Zürich, Switzerland. This position suits engineering professionals who want to train base models through advanced post-training stacks to deliver production Claude models.
Key Responsibilities
Requirements
Nice to Have
Skills and Tools
Salary and Benefits
About the Company
Anthropic is an AI safety and research company working to create reliable, interpretable, and steerable AI systems that are beneficial for society. Operating as a public benefit corporation, the organization approaches AI research as an empirical science resembling physics or biology. Teams collaborate closely on large-scale research initiatives focusing on long-term goals such as scalable alignment, interpretability, and robust model behavior.
How to Apply
Candidates should apply directly on the official Anthropic careers page to submit their application and learn more about open positions.
Skills and tech stack
Questions candidates ask
Where is this role located?
This position is located in Zürich, Switzerland, and follows a location-based hybrid policy requiring staff to be in the office at least 25% of the time.
What programming language is required for the interview process?
All interviews for this role are conducted in Python.
Does Anthropic offer visa sponsorship for this position?
Yes, Anthropic does sponsor visas for this role and retains an immigration lawyer to assist candidates who receive an offer.
What level of experience is required?
Anthropic welcomes candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems. Exact years of experience correlate with internal job level requirements.
What are the core responsibilities of this position?
Responsibilities include scaling and improving post-training techniques like Constitutional AI and RLHF, building robust fine-tuning pipelines, debugging complex training issues, and translating emerging research into production-ready implementations.