Principal Applied Scientist, Agentic AI
Tasks
- Define evaluation metrics for safety quality and win rates
- Design post training pipelines with supervised fine tuning and preference modeling
- Develop reward models and objective formulations
- Improve training and evaluation efficiency using off policy evaluation and replay
- Lead RL post training strategy for production models
- Mentor scientists and engineers in RL post training and evaluation
- Translate conversational logs and behavioral signals into training signals
Perks/Benefits
Skills/Tech-stack
AI Feedback | DPO | Fine Tuning | Human Feedback | Learning from Human Feedback | Multi-Modal | Multi-Objective Optimization | Multi-modal Models | Objective optimization | Off-policy evaluation | Pairwise Ranking | Policy Evaluation | Post-training | Preference Modeling | RLAIF | RLHF | Reinforcement Learning | Reinforcement Learning from AI Feedback | Reinforcement Learning from Human Feedback | Reward Modeling | Supervised Fine Tuning | Transformer Models | Vector Search
Education
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