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AI Research Scientist, New Grad – Agents & Reinforcement Learning

Snowflake·$176,000 – $230,000

Primary stack

Python

Nice to have's

recursive self-improvement frameworksautomated AI scientist paradigmslarge-scale distributed trainingmathematical reasoningdomain-specific AI applications

Job description

About the Position

AI Research Scientist, New Grad – Agents & Reinforcement Learning

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.


Responsibilities

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

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 Have

  • 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

We Offer

Snowflake's AI Research team operates at the frontier of what autonomous AI systems can do - not as a research lab disconnected from practice, but as a team where your work ships into a platform used by thousands of enterprises. You'll be working alongside researchers and engineers who are building the next generation of agentic infrastructure, with access to large-scale compute, real-world data challenges, and the shortest possible path from research idea to product impact.

For a new grad role, this is a rare opportunity to grow your research career while contributing to systems that actually run in production - shaping how AI agents reason, learn, and improve themselves inside the world's leading data cloud.

Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.


About the Company

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.


Compensation

  • Compensation Range: $176K - $230K

For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com

© Snowflake. This job description was sourced from the employer's public career page. TheJob is not the employer — we index the posting and route candidates to the source. All content rights and hiring decisions belong to the employer.

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Snowflake

Cloud Computing,Data Management,Artificial Intelligence

About the Company Snowflake is a cloud-native data platform launched in 2012 by a team of data experts who identified limitations in traditional databases. The company has a strong presence in Poland, with a technical hub in Warsaw. Snowflake offers an international work environment with opportunities for skill development and collaboration with industry experts. Products, Services & Tech Stack Core Solutions: Data processing and management platform. Technologies & Frameworks: Specific technologies and frameworks are not detailed in the provided text. Project Scope & Target Clients Client Base: Not specified. Engineering Scale: Not specified.

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