大模型算法工程师（开放域对话）
Tasks
- Accelerate inference with quantization distillation and efficient serving
- Apply GRPO PPO and DPO for policy optimization
- Build end to end dialogue data pipelines
- Clean and deduplicate raw corpora
- Collaborate with engineering for model deployment on edge and cloud
- Create evaluation datasets for interactive scenarios
- Develop LLM algorithms for open domain dialogue
- Fine tune base models with SFT
- Implement multi turn dialogue state tracking
- Improve multi turn decision planning accuracy
- Optimize intent recognition and personalization metrics
- Optimize models with RLHF and RLAIF
- Perform prompt engineering for better tool use
- Reduce hallucinations in agent tool use
- Run offline evaluation and online A B testing
- Train reward models for reinforcement learning
Perks/Benefits
Skills/Tech-stack
AI Feedback | Agentic tool use | DPO | DST | DeepSpeed | Dialogue State Tracking | Distributed Training | Function Calling | GRPO | Human Feedback | Inference acceleration | LLM | Language Models | Large Language Models | Learning from Human Feedback | Model Distillation | Model Quantization | Multi Turn Dialogue State Tracking | Multi-turn dialogue | PPO | Prompt engineering | Python | RLAIF | RLHF | React | Reinforcement Learning | Reinforcement Learning from AI Feedback | Reinforcement Learning from Human Feedback | Reward Modeling | SFT | State tracking | Thought Intermediate Result | Tool use | VLLM
Education
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