大语言模型后训练/Agentic算法工程师
Tasks
- Build agentic reinforcement learning training
- Build training and interaction environments
- Create reward function and agent harness
- Design reward modeling and preference learning workflows
- Develop LLM post-training pipelines
- Develop user simulator and world model
- Implement multi tool calling and function calling
- Optimize multi turn dialogue agents
- Perform error attribution and方案 optimization
- Run offline evaluation and online analysis
Perks/Benefits
- N/A
Skills/Tech-stack
Agentic RL | Distributed Training | Function Calling | GRPO | Java | Language Processing | Learning frameworks | Memory | Multi-turn dialogue | Natural Language | Natural Language Processing | On Policy | On policy Distillation | OpenRLHF | PPO | Planning | Policy Distillation | Preference Learning | Python | RLHF | RLVR | React | Reflection | Reinforcement Learning | Reinforcement Learning Frameworks | Reward Modeling | Tool Integrated Reasoning | Tool use | TypeScript
Education
Regions
Countries
States
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