LLM & Agent Algorithm Graduate (Search) - 2027 Start (PhD)
Tasks
- Apply object detection and OCR for search experiences
- Build agent loop training inference evaluation closed loop
- Build reward verifier systems and automated evaluation frameworks
- Build search-domain LLM foundation
- Develop multi agent orchestration and scheduling algorithms
- Develop post training pipeline for search LLMs
- Develop self improvement and recursive self improvement pipelines
- Develop visual search and on device intelligence features
- Evaluate long cycle complex agent tasks
- Implement agent execution framework for tool calling planning memory and environment interaction
- Implement long term memory mechanisms for LLMs
- Implement multimodal AIGC creation workflows
- Optimize LLM inference for long context
- Perform data synthesis and quality control for vertical domains
Perks/Benefits
- N/A
Skills/Tech-stack
AI Feedback | Agent Orchestration | Agent systems | Automated Evaluation | Computer Vision | Context modeling | Data Quality | Data quality control | Deep learning | Distributed Training | Fine Tuning | Human Feedback | Knowledge Distillation | Language Models | Language Processing | Large Language Models | Learning from Human Feedback | Long Context | Long Context Modeling | Long-term Memory | Machine Learning | MapReduce | Mixed Precision | Mixed-precision training | Model Alignment | Model Distillation | Model Pruning | Model Quantization | Multi-Agent | Multi-Agent Systems | Multimodal Learning | Natural Language | Natural Language Processing | Planning | Pretraining | PyTorch | Quality Control | RAG | Recursive Self Improvement | Reinforcement Learning | Reinforcement Learning from AI Feedback | Reinforcement Learning from Human Feedback | Self-Improvement | Spark | TensorFlow | TensorRT | Tool-Calling
Education
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