Research, Post-Training Data
San Francisco Bay Area
USD 295K-365K (estimate) Senior-level Full Time
Tasks
- Build scalable human labeling pipelines
- Conduct experiments with scientific rigor
- Design and run evaluation loops
- Design data quality alignment benchmarks
- Design post training data collection strategy
- Develop preference modeling methods
- Generate synthetic data examples
- Implement model assisted labeling
- Measure real world impact of post training
- Optimize evaluations and metrics
- Publish and present research
- Research human AI interaction paradigms
- Share code and datasets
- Synthesize human feedback data
Perks/Benefits
Skills/Tech-stack
AI Feedback | Active Learning | Data Curation | Data labeling | Deep learning | Distributed Training | Evaluation Design | Human Feedback | Learning from Human Feedback | Machine Learning | Metrics and benchmarks | Model-assisted labeling | Preference Modeling | Probability | PyTorch | Python | Reinforcement Learning | Reinforcement Learning from AI Feedback | Reinforcement Learning from Human Feedback | Reward Learning | Statistics | Synthetic data | TensorFlow
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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