大语言模型后训练/Agentic算法工程师
Tasks
- Build LLM agents for in car intelligence
- Build data production and failure case feedback loops
- Build training and interaction environments
- Conduct offline evaluation and online analysis
- Create user simulator and world model
- Design reward functions and evaluation frameworks
- Develop multi turn dialogue agents
- Implement agentic reinforcement learning
- Optimize tool calling for multi tool tasks
- Perform LLM post training
- Run preference learning
- Trace errors and improve model performance
- Train reward models
Perks/Benefits
- N/A
Skills/Tech-stack
Agentic RL | DAPO | Distributed Training | Evaluation Frameworks | Function Calling | GRPO | Inference Serving | Java | Language Processing | Level optimization | Long Range | Long Range Task Learning | Machine Learning | Memory | Multi-turn dialogue | Natural Language | Natural Language Processing | On Policy | On policy Distillation | OpenRLHF | PPO | PPO RL | Planning | Policy Distillation | Preference Learning | Python | RLHF | RLVR | React | Reflection | Reinforcement Learning | Reward Modeling | Sparse Reward | Sparse Reward Modeling | Tool Integrated Reasoning | Training frameworks | Trajectory Level Optimization | TypeScript | VeRL
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Regions
Countries
States
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