Description
As a Research Engineer on our Post-Training team, 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.
You'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with.
Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
We conduct all interviews in Python, and 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
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:
Thrive in controlled chaos and are energized, rather than overwhelmed, when juggling multiple urgent priorities
Adapt quickly to changing 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
Strong candidates may also:
Have experience with LLMs
Have a keen interest in AI safety and responsible deployment
We welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems.
However, proficiency in Python, deep learning frameworks, and distributed computing is required for this role.
The annual compensation range for this role is $350,000-$500,000 USD.