We are hiring an AI Research Scientist (New Grad) for our AI Research team. Our team is pushing the frontier of autonomous, self-improving AI systems - building agents that reason, code, and learn at scale inside the Snowflake Data Cloud. This role sits at the intersection of agentic AI and reinforcement learning, where your research will directly shape how enterprises leverage intelligent automation. AS AN AI RESEARCH SCIENTIST AT SNOWFLAKE, YOU WILL: - 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 OUR IDEAL AI RESEARCH SCIENTIST WILL HAVE: - 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 BONUS POINTS FOR THE FOLLOWING: - 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