7 months ago
Responsibilities
- Implement and optimize post-training techniques at scale on frontier models
- Conduct research to develop and optimize post-training recipes that improve production model quality
- Design, build, and run robust pipelines for model fine-tuning and evaluation
- Develop tools to measure and improve model performance across multiple dimensions
- Translate emerging research techniques into production-ready implementations
- Debug complex training-pipeline and model-behavior issues
- Establish best practices for reliable and reproducible model post-training
Requirements
- Bachelor’s degree in a related field or equivalent experience
- Strong software engineering skills and experience building complex ML systems
- Proficiency in Python, deep learning frameworks, and distributed computing
- Experience with large-scale distributed systems and high-performance computing
- Experience training, fine-tuning, or evaluating large language models
- Ability to analyze and debug model training processes and balance research exploration with engineering rigor
- Strong collaboration, communication, prioritization, and ambiguity-navigation skills
- Interest in AI safety and responsible deployment is valued
Benefits
- Hybrid work arrangement with staff expected to be in an office at least 25% of the time
- Visa sponsorship may be available, with immigration lawyer support
- Competitive compensation and benefits
- Optional equity donation matching
- Generous vacation and parental leave
- Flexible working hours
- Collaborative office space
Tech Stack
Categories
AI ResearchML Engineering
About Anthropic
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.