Tasks:
- * Build long-term memory and continual learning systems
- * Design LLM agent architectures
- * Develop environment interaction and tool-use mechanisms
- * Develop multi-agent collaboration systems
- * Evaluate agent performance and safety
- * Integrate and deploy agent algorithms
- * Optimize agent reasoning and planning
Perks/Benefits:
Skills/Tech stack required:
[Agent systems] [Autogen] [Chain-of-Thought] [Chain-of-Thought prompting] [Distributed Computing] [Docker] [DQN] [Few-shot] [Few-Shot Learning] [JAX] [Knowledge graphs] [Kubernetes] [Langchain] [Language Models] [Large Language Models] [MDP] [Multi-Agent] [Multi-Agent Systems] [Policy Gradient] [PPO] [Prompt engineering] [Prompt Tuning] [Python] [PyTorch] [Reinforcement Learning] [REST APIs] [RLAIF] [RLHF] [TensorFlow] [TensorRT] [Transformer Architecture] [Vector Databases] [VLLM]
Educational requirements:
[Master's Degree] [PhD]
Role(s):
[Agent Engineer] [AI/Agent Engineer] [AI Research Engineer] [Engineer] [Language Model Engineer] [Large Language Model Engineer] [Learning Engineer] [Machine Learning Engineer] [Model Engineer] [Research Engineer]