Research Engineers, Post-Training
Distyl AI, San Francisco +1
What you will do
- Design and run post-training workflows that improve the behavior, reliability, and usefulness of AI systems
- Develop datasets, preference signals, evaluation suites, reward models, fine-tuning workflows, and feedback loops for applied AI use cases
- Investigate how different post-training techniques affect system behavior across enterprise workflows and production constraints
- Build infrastructure for experimentation, model comparison, regression testing, and behavior analysis
- Partner with AI Researchers to explore new post-training methods and with AI Engineers to apply successful techniques in deployed systems
- Analyze model outputs, failure modes, human feedback, and production traces to identify opportunities for behavioral improvement
- Create repeatable processes for adapting AI systems to customer domains while preserving robustness, transparency, and maintainability