Research Engineer, Production Model Post-Training
San Francisco, CA | New York City, NY | Seattle, WA
Posted 16d ago
Job Location
San Francisco, CA | New York City, NY | Seattle, WA
Tech Stack
Remote Work Policy
On-site
Categories
AI Research Engineer
About the job
Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with. You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models. For this role, interviews are conducted in Python, and the position may require responding to incidents on short notice, including weekends.
Responsibilities
- Implement and optimize post-training techniques at scale on frontier models.
- Conduct research to develop and optimize post-training recipes that directly improve production model quality.
- Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation.
- Develop tools to measure and improve model performance across various dimensions.
- Collaborate with research teams to translate emerging techniques into production-ready implementations.
- Debug complex issues in training pipelines and model behavior.
- Help establish best practices for reliable, reproducible model post-training.
Requirements
- Strong software engineering skills with experience building complex ML systems.
- Comfortable working with large-scale distributed systems and high-performance computing.
- Experience with training, fine-tuning, or evaluating large language models.
- Ability to balance research exploration with engineering rigor and operational reliability.
- Adept at analyzing and debugging model training processes.
- Enjoy collaborating across research and engineering disciplines.
- Ability to navigate ambiguity and make progress in fast-moving research environments.
- Proficiency in Python.
- Proficiency in deep learning frameworks.
- Proficiency in distributed computing.
Benefits
- Annual compensation range: $350,000 - $500,000 USD
- Visa sponsorship available