Research Engineer, Production Model Post-Training
Anthropic · San Francisco, CA | New York City, NY | Seattle, WA · AI Research & Engineering
About this role
Anthropic is hiring a mid-level Research Scientist in the machine learning function based in San Francisco, CA | New York City, NY | Seattle, WA. The posting calls out experience with Python, LLMs, Deep Learning, Distributed Systems. Compensation is listed at $350,000–$500,000 per year.
- Role
- Research Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- San Francisco, CA | New York City, NY | Seattle, WA
- Department
- AI Research & Engineering
More roles at Anthropic
Job description
from Anthropic careersAbout 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
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.
Note: For this role, we conduct all interviews in Python. This role may require responding to incidents on short-notice, including on 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