Research Engineer, Codex
Tasks
- Build evals and environments to expose model failures
- Debug model failures and propose hypotheses and fixes
- Design and run experiments to improve agentic model behavior
- Develop data mixtures and synthetic data for model behavior
- Improve large scale training and launch systems
- Own end to end improvements to the post training stack
- Translate product signal into model improvements
Perks/Benefits
- N/A
Skills/Tech-stack
AI Feedback | Agent systems | Behavior Analysis | Computer use | Cost Optimization | Data Pipelines | Evaluation | Function Calling | Human Feedback | Language Models | Large Language Models | Latency optimization | Learning from Human Feedback | Machine Learning | Model Training | Model behavior | Model behavior analysis | Multi-Agent | Multi-Agent Systems | Observability | Post-training | RLAIF | RLHF | Reinforcement Learning | Reinforcement Learning from AI Feedback | Reinforcement Learning from Human Feedback | Reproducibility | Synthetic data | Tool use
Education
N/A
Roles
Regions
Countries
States
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