Responsibilities of the Candidate:

Conduct foundational research on multi-agent reinforcement learning, agent exploration, continual learning, and scalable alignment strategies.
Design and evaluate algorithms that improve the learning efficiency, coordination, and controllability of large AI systems.
Develop methods for interpreting and analyzing internal model behavior, including representation analysis, circuit discovery, and causal reasoning tools.
Investigate alignment techniques such as reward modeling, safety fine-tuning, pruning, unlearning, and training-time behavioral control.
Explore information-theoretic and causal approaches for understanding how models represent knowledge and make decisions.
Build research infrastructure and experimental frameworks for RL training, alignment experiments, and large-scale evaluation.
Publish research in leading venues and contribute to open-source tools and alignment research infrastructure.

Requirements:

PhD or Masters or equivalent research experience in Machine Learning, Artificial Intelligence, Computer Science, Mathematics, Physics, or related fields.
Strong background in reinforcement learning, multi-agent systems, large language models, interpretability, deep learning theory, or information-theoretic ML.
Proven ability to conduct independent research and publish in top-tier AI conferences such as NeurIPS, ICML, ICLR, ACL, CVPR, or AAAI.
Hands-on experience training and experimenting with models using frameworks such as PyTorch, JAX, or TensorFlow.
Strong programming and experimentation skills in Python and modern ML research tooling.
Demonstrated interest in AI alignment, safety, interpretability, or trustworthy AI systems.