AnthropicResearch Engineer, Production Model Post Training
Develop and optimize systems for post-training AI models, implementing techniques like Constitutional AI and RLHF.
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems.
About the role
Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety.
Responsibilities:
- 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
You may be a good fit if you:
- 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
We welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems.
The expected salary range for this position is:
Annual Salary:
$315,000—$510,000 USD
Logistics
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.**
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate.
How we're different
We believe that the highest-impact AI research will be big science.