AI Research Scientist, New Grad – Agents & Reinforcement Learning

Snowflake•Bellevue, WA

4d•$176,000 - $230,000

About The Position

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Snowflake is about empowering enterprises to achieve their full potential, and people too. With a culture that's all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology and careers to the next level. 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.

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

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