AI Research Scientist, New Grad – Agents & Reinforcement Learning

Responsibilities

• Design and develop agentic frameworks powered by recursive self-improvement loops, enabling AI systems that iteratively refine their own capabilities and strategies
• Build and evaluate auto research agents — systems capable of autonomously formulating hypotheses, executing experiments, and synthesizing findings
• Develop coding agents that understand, generate, and debug code across complex, multi-step programming tasks
• Conduct research in reinforcement learning with a focus on RLHF, DPO, and PPO as mechanisms for aligning and improving agentic behaviors
• Contribute to multi-agent systems where specialized agents collaborate, negotiate, and self-organize to solve enterprise-scale problems
• Develop and curate training data pipelines — both synthetic and human-annotated — to support novel agentic and RL research domains
• Publish research findings at top-tier venues such as NeurIPS, ICML, ICLR, and ACL

Requirements

• PhD in Computer Science, Machine Learning, Artificial Intelligence, or a closely related field (completing or recently completed; or equivalent research experience)
• Foundational expertise in reinforcement learning algorithms, including RLHF, DPO, PPO, or multi-agent systems
• Research experience in LLM post-training, fine-tuning, or reasoning model development
• Demonstrated ability to implement and experiment with agentic architectures — including tool-use, planning, and self-correction loops
• Proficiency in Python and at least one deep learning framework (PyTorch or JAX strongly preferred)
• Strong mathematical and analytical foundation — comfortable working at the intersection of theory and empirical research
• At least one first-author or co-authored publication or preprint in a relevant AI/ML area

Nice-to-Haves

• Hands-on experience building or evaluating coding agents or auto research agents
• Familiarity with recursive self-improvement frameworks or automated AI scientist paradigms
• Experience with large-scale distributed training or efficient training paradigms
• Background in mathematical reasoning, structured decision-making, or program synthesis
• Exposure to domain-specific AI applications in healthcare, finance, or enterprise workflows