Anthropic · San Francisco/New York City/Seattle · Hybrid
Research Engineer, Production Model Post Training
5.4.2025
Description
- Implement and optimize post-training techniques at scale on frontier models
- 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
Qualifications
- Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent priorities
- Maintain clarity when debugging complex, time-sensitive issues
- Have strong software engineering skills with experience building complex ML systems
- Are comfortable working with large-scale distributed systems and high-performance computing
- Have experience with training, fine-tuning, or evaluating large language models
- Can balance research exploration with engineering rigor and operational reliability
- Are adept at analyzing and debugging model training processes
- Enjoy collaborating across research and engineering disciplines
- Can navigate ambiguity and make progress in fast-moving research environments
- Have a keen interest in AI safety and responsible deployment
- Experience with LLMs is a significant plus
- Proficiency in Python, deep learning frameworks, and distributed computing is required for this role
Benefits
$315,000 - $340,000 USD