Researcher, Context - Agent Post-Training
Tasks
- Analyze qualitative model behavior and formulate hypotheses and experiments
- Build evals and environments for model failure discovery
- Convert model failures into training data and fixes
- Create synthetic data and eval loops
- Debug hard failures in shipped models
- Define data mixtures and objectives for training
- Design experiments to improve scaling of compute on context
- Implement early training and alignment interventions
- Improve large scale training and launch reliability observability and reproducibility
- Own end to end improvements to post training stack
- Run experiments to improve scaling of compute on context
- Translate product signal into model improvements
Perks/Benefits
- N/A
Skills/Tech-stack
Data Pipelines | Deep learning | Experimentation | Grading systems | Language Models | Large Language Models | Latency optimization | Machine Learning | Model Evaluation | Observability | Post-training | Production Machine Learning | Programming | RLAIF | RLHF | Reinforcement Learning | Reproducibility | Software Engineering | Statistics | Synthetic data | Systems engineering
Education
N/A
Regions
Countries
States
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