Agent Post-Training Research
Tasks
- Build evals and environments to identify model failures
- Debug model failures and define hypotheses experiments and fixes
- Decide integrations and fixes for major model runs
- Design and run experiments to improve agent behavior
- Develop training signal with data mixtures objectives and synthetic data
- Improve training and launch reliability observability and reproducibility
- Own post training improvements end to end
- Translate product signal into model improvements
Perks/Benefits
- N/A
Skills/Tech-stack
AI Feedback | Agent systems | Calibrated Reasoning | Data Pipelines | Deep learning | Experiment design | Factuality | Function Calling | Grader Systems | Human Feedback | Instruction following | Language Models | Large Language Models | Learning from Human Feedback | Machine Learning | Model Evaluation | Multi-Agent | Multi-Agent Systems | Post-training | RLAIF | RLHF | Reinforcement Learning | Reinforcement Learning from AI Feedback | Reinforcement Learning from Human Feedback | Software Engineering | Statistical Analysis | Synthetic data | Tool use
Education
Regions
Countries
States
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