Senior Applied Scientist
Tasks
- Build conversational AI simulations
- Build reward models
- Build verifiers
- Create LLM as judge pipelines
- Design RLHF methods
- Design RLVR methods
- Develop RL/post-training pipelines
- Evaluate post training model performance
- Implement DPO methods
- Research factors driving post training gains
- Run synthetic data healthcare RL training experiments
Perks/Benefits
- N/A
Skills/Tech-stack
Direct Preference Optimization | Distributed Training | Human Feedback | LLM-as-a-Judge | Language Models | Language Processing | Large Language Models | Learning from Human Feedback | Machine Learning | Natural Language | Natural Language Processing | Preference optimization | PyTorch | Python | Reinforcement Learning | Reinforcement Learning from Human Feedback | Reward Models | Verifiers
Education
Regions
Countries
States
Cities
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