大语言模型后训练/Agentic算法工程师
Tasks
- Build agent training and interaction environments
- Build large language model agent
- Conduct training and evaluation
- Create task orchestration and automated evaluation
- Design reward functions and harnesses
- Develop agentic reinforcement learning
- Develop tool systems and user simulators
- Drive LLM post training
- Implement multi tool calling
- Optimize multi turn reasoning and decision making
- Perform error analysis and scheme optimization
- Reproduce frontier post training methods
- Research and improve vehicle dialogue systems
- Set up data production and feedback loops
Perks/Benefits
- N/A
Skills/Tech-stack
Distributed Training | Function Calling | Java | Language Models | Language Processing | Large Language Models | Machine Learning | Memory | Model Evaluation | Natural Language | Natural Language Processing | On Policy | On policy Distillation | OpenRLHF | PPO | Planning | Policy Distillation | Preference Learning | PyTorch | Python | RLHF | RLVR | React | Reflection | Reinforcement Learning | Reinforcement Learning Framework | Reward Modeling | TRL | Tool-Calling | TypeScript | VeRL
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Regions
Countries
States
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