AI Research Scientist

Role Overview

Colleague AI is hiring an AI Research Scientist to help advance the next generation of agentic AI systems for K–12 education. This position will be in-person only, located in Kirkland, WA.

This role is ideal for a researcher who combines strong technical depth with curiosity, fast learning, and an open-minded approach to applied research. You will work on cutting-edge problems at the intersection of large language models (LLMs), AI agents, learning environments, evaluation, human-AI collaboration, and education technology.

A key goal of this role is to produce high-quality applied AI research and publications that bring frontier AI-agent ideas into real-world K–12 settings. Examples of relevant research directions include agent learning from classroom environments, long-horizon educational workflows, AI tutoring and grading agents, benchmark design, feedback-driven improvement, multi-agent teacher/student simulations, and rigorous evaluation of AI systems in authentic educational use cases.

You will not only explore new research ideas, but also help translate them into production systems used by teachers, students, and school leaders.

Responsibilities

• Lead applied research on LLMs, AI agents, evaluation systems, and educational AI.
• Develop research prototypes that apply frontier AI-agent methods to K–12 teaching and learning workflows.
• Design rigorous evaluation frameworks for AI tutoring, grading, lesson generation, classroom agents, and other educational use cases.
• Build benchmarks and datasets that measure long-horizon agent behavior, feedback-driven learning, reliability, safety, and instructional quality.
• Study how agents perform in realistic educational environments involving rubrics, standards, student work, teacher feedback, classroom context, and multi-step workflows.
• Design experiments to measure model behavior, diagnose failures, compare approaches, and improve system performance.
• Collaborate with engineering and product teams to turn promising research into production-ready features.
• Publish research papers, technical reports, benchmarks, datasets, or open-source tools that establish Colleague AI as a thought leader in AI for education.
• Stay current with advances in LLMs, agentic AI, retrieval-augmented generation, evaluation methods, synthetic data, AI safety, and learning sciences.
• Help establish internal best practices for model evaluation, experimentation, monitoring, and continuous improvement.

Qualifications

• Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Natural Language Processing, Data Science, Learning Sciences, Educational Technology, or a related technical field.
• Strong research background in machine learning, deep learning, NLP, large language models, AI agents, or human-AI interaction.
• Experience optimizing model performance in production or production-like environments.
• Strong programming skills in Python and experience with ML frameworks such as PyTorch, TensorFlow, JAX, or similar.
• Experience designing experiments, evaluation frameworks, benchmarks, or datasets.
• Strong understanding of model validation, statistical analysis, error analysis, and empirical research methods.
• Ability to move between open-ended research questions and practical implementation.
• Clear technical writing skills, with the ability to produce papers, reports, documentation, and research narratives.
• Strong collaboration skills and interest in working with engineers, product teams, educators, and school partners.

Preferred Qualifications

• Publications in top-tier AI, ML, NLP, HCI, learning analytics, or education technology venues.
• Experience with LLM agents, tool-using agents, multi-agent systems, autonomous workflows, or long-horizon agent evaluation.
• Experience with educational AI, intelligent tutoring systems, automated feedback, grading, curriculum generation, or classroom technology.
• Experience building benchmark environments, simulation environments, evaluation harnesses, or reproducible research systems.
• Experience with retrieval-augmented generation, fine-tuning, synthetic data generation, data annotation, or model adaptation.
• Experience evaluating AI systems for safety, reliability, fairness, privacy, or age-appropriate behavior.
• Experience deploying research into production or working in a startup environment.
• Familiarity with K–12 education standards, classroom workflows, LMS/SIS systems, or teacher-facing software is a plus.

Compensation

Colleague AI offers competitive compensation, including base salary, equity, and benefits. Final compensation will be determined based on experience, qualifications, location, and role scope.

How to Apply

Please submit the following materials:

• A brief statement of interest describing your relevant research interests, technical experience, and motivation for joining Colleague AI.
• Evidence of qualifications, such as a portfolio of prior work, selected publications, GitHub repositories, technical writing, demos, or deployed systems.
• A current CV.

Applications should apply via this link.

Deadline

We will start to review applications after 8/31/2026. The position will remain open until it is filled. The starting date will be immediately after the position is filled.