Senior Applied Scientist
Tasks
- Accelerate inference for generative models
- Build generative model evaluation pipelines
- Collaborate with product teams on technology transfer
- Conduct post training research
- Design and evaluate RLHF
- Design and evaluate direct preference optimization
- Design and evaluate supervised fine tuning
- Develop distillation and compression methods
- Develop preference optimization methods
- Develop reward modeling methods
- Evaluate models for quality efficiency safety
- Implement research ideas in Python
- Mentor peers and junior researchers
Perks/Benefits
- N/A
Skills/Tech-stack
Diffusion Models | Direct Preference Optimization | Fine Tuning | Human Feedback | Inference acceleration | Language Models | Large Language Models | Learning from Human Feedback | Machine Learning | Model Compression | Model Distillation | Preference optimization | PyTorch | Python | Reinforcement Learning | Reinforcement Learning from Human Feedback | Reward Modeling | Supervised Fine Tuning | Transformers
Education
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