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About This Role
Location: Greater Seattle Area
Compensation: $120\-200K
Full\-Time \| Permanent
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.
Apply Now: Send your resume and a brief introduction to [email protected]
Job Type: Full\-time
Pay: $120,000\.00 \- $200,000\.00 per year
Benefits:
- 401(k)
- Health insurance
- Paid time off
- Parental leave
- Vision insurance
Work Location: In person
Salary Context
This $120K-$200K range is below the median for Research Scientist roles in our dataset (median: $183K across 83 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 3,708 AI roles we're tracking, Research Scientist positions make up 3% of the market. At Colleague AI, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $222,200 based on 197 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($160K) sits 28% below the category median. Disclosed range: $120K to $200K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Colleague AI AI Hiring
Colleague AI has 1 open AI role right now. They're hiring across Research Scientist. Based in Kirkland, WA, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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