Persons in these roles are expected to work from our offices in Seattle. On-site requirements vary based on position and team. If you have questions about on-site work arrangements for this role, please ask your recruiter.

Our base salary range is $167,030 - $260,570, and in addition we have generous bonus plans to provide a competitive compensation package.

Who You Are

You are a creative, hands-on AI researcher with a strong foundation in AI and reasoning. Passionate about pushing the boundaries of research and engineering, you seek to advance AI both scientifically and openly. You thrive in collaborative environments, take initiative, and are eager to contribute to Ai2’s impact-driven mission in a dynamic, fast-paced research setting. Skilled at translating complex concepts into concrete research goals and implementation steps, you define clear approaches, execute effectively, and communicate results with clarity. You leverage these skills to create real-world benefits for researchers and practitioners alike and are excited to help drive the development of the best-performing open AI model.

Who We Are

We are a non-profit AI institute, focused on developing foundational AI research and innovation to deliver real-world impact through large-scale open models, the science of agentic systems, data, and artifacts (e.g. asta.allen.ai, Olmo, Tulu). We unite the best and brightest scientific and engineering minds to explore the potential of truly open AI. Through our efforts we endeavor to empower academics, researchers, and AI developers more broadly to advance language models and reasoning-based agentic systems. Through close collaboration, we rapidly identify, define, and act on the most exciting and promising new ideas in AI.

Our team is focused on developing Asta, an agentic system aimed at revolutionizing how science is performed. We engage in language model post-training, generating and curating data, layering control strategies atop LLMs, building systems that learn continually and improve themselves, and developing novel methodologies to push the field forward. We study and evaluate AI models both theoretically and empirically, aiming to advance their capabilities. Additionally, we create impactful real-world applications, such as in scientific synthesis. Our goal is to develop state-of-the-art models and harnesses that excel in scientific reasoning spanning literature synthesis, hypothesis formation, scientific coding, execution of computational experiments, and data analysis. These systems will collaborate with human scientists, not replace them.

Your Next Challenge

As a key member of the core team of research scientists and machine learning engineers at Ai2, you will drive the development of AI systems with deep reasoning capabilities with a focus on the huge breadth of skills necessary for scientific reasoning. In this role, you will lead foundational research to advance agentic systems, shape our strategic direction, and lay the groundwork for future innovations. You will also work towards long-term ambitious research goals, while identifying intermediate milestones.

The essential functions include, but are not limited to the following:

Contribute to Ai2’s open source research environment to innovate, study and advance the science of generative AI and agentic systems.
Take a leading role in building artifacts and implementing software systems with real-world impact
Author and present high-quality scientific technical reports, papers, and presentations
Collaborate with and learn from team members across Ai2, including scientists and engineers
Mentor early-career researchers and interns on their projects
Develop collaborative relationships with relevant academic, industrial, government, and standards organizations

What You’ll Need

A PhD focusing on machine learning, reasoning, natural language processing, human-computer interaction, or a related area, with expertise in one or more of the following: agentic orchestration / systems, interactive and intelligent interfaces, reinforcement learning, experimental methodology, language models, the science of deep learning, or efficient algorithms for deep learning. Curiosity about all of the above is a plus.