Agent Post-Training, API & Power Users
Tasks
- Build evaluation systems graders and environments from real workflows
- Convert observed model failures into training data
- Create feedback loops using power user traces and API usage patterns
- Debug hard model failures using traces evals training data and product context
- Design and run model behavior experiments for API workflows
- Develop behavior improvement projects from failure analysis to integration and launch readiness
- Develop training and alignment interventions using objectives and synthetic data
- Improve large scale training and launch reliability observability and cost latency
- Improve tool use planning and long horizon execution reliability
- Lead cross functional projects for multi agent systems and production like environments
Perks/Benefits
- N/A
Skills/Tech-stack
AI Feedback | Agent systems | Computer use | Cost Optimization | Data Generation | Deep learning | Evaluation | Experimentation | Function Calling | Human Feedback | Language Models | Language Processing | Large Language Models | Latency optimization | Learning from Human Feedback | Long Horizon Execution | Machine Learning | Model Debugging | Model Training | Multi-Agent | Multi-Agent Systems | Natural Language | Natural Language Processing | Observability | Planning | Reinforcement Learning | Reinforcement Learning from AI Feedback | Reinforcement Learning from Human Feedback | Reproducibility | Statistical modeling | Synthetic data | Tool use
Education
N/A
Regions
Countries
States
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