This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a PhD-level researcher for a 10–12 week summer internship focused on a critical technical and design challenge: Educational AI Memory. The selected candidate will define how an AI system effectively retains, synthesizes, and utilizes long-term context from multiple perspectives to drive truly personalized education. We are building an advanced AI platform designed to facilitate long-term educational planning and decision-making across three key stakeholders: Students, Parents, and Teachers. This role combines theoretical framework design, applied AI prototyping, and empirical validation in a live product environment. Key Responsibilities - Framework Design: Develop a theoretical framework for “Triangulated AI Memory” within an educational context. - Applied Research: Collaborate with the engineering team to prototype memory architectures (e.g., RAG systems, vector stores, long-term context management) aligned with pedagogical objectives. - User Modeling: Explore and formalize methods for representing evolving student, parent, and teacher perspectives within AI systems. - Validation Studies: Design and conduct qualitative and/or quantitative research to evaluate system utility, trust, and personalization effectiveness. - Product Translation: Translate research concepts into actionable technical specifications. - Publication: Contribute to a co-authored paper targeting top-tier venues (e.g., CHI, LAK, EDM, NeurIPS) based on the summer’s research. Qualifications - Currently enrolled in a PhD program in Computer Science, Human-Computer Interaction, Artificial Intelligence, Learning Sciences, or a related field, with a research focus in machine learning, memory architectures, user modeling, or human-centered AI. - Strong track record of research in AI and machine learning. - Demonstrated experience in User Modeling, Recommender Systems, Large Language Models (LLMs), or related areas. - Solid understanding of the ethical, privacy, and design implications of AI in human-centered environments. - Ability to translate theoretical research concepts into clear, actionable product and technical specifications. Benefits - 401(k) matching - Flexible schedule - Opportunity for advancement Company Description 7EDU is an education technology organization dedicated to long-term academic planning and student development. For over a decade, we have supported K–12 learners through structured advising, college admissions strategy, and personalized mentorship, while building proprietary tools and frameworks to support scalable, data-informed guidance. Our AI systems are designed to support longitudinal educational decision-making — integrating student progress, parental input, and instructional context to enable personalized, responsible, and evidence-based support. Application Process Please email your application to: hiringteam@7edu.org - Email Subject: AI Research Internship FULL NAME - Resume - A brief cover letter outlining your research interests regarding AI memory or user modeling. - A link to Google Scholar or an attachment of one relevant writing sample/paper. Flexible work from home options available.

This role involves working as an Automation & AI Co-op. Number of Positions: 1 Date: May 2026 – May 2027 Duration: 10 months Location: Remote - US

This role involves spearheading the internal Agentic AI revolution at Lookout, Inc. You will be the primary architect responsible for operationalizing AI across various business functions. Design, Build and Maintain Agentic Workflows: Architect and deploy autonomous AI agents using low-code/no-code orchestration platforms (e.g., N8N) and enterprise LLMs (e.g., Gemini Enterprise). Architect and deploy autonomous AI agents using low-code/no-code orchestration platforms (e.g., N8N) and enterprise LLMs (e.g., Gemini Enterprise). Translate Business Needs to AI Logic: Act as the bridge between non-technical stakeholders and AI capabilities. Dissect requests from departments like Finance or Legal and re-engineer their processes for compatibility with LLMs and agents. Act as the bridge between non-technical stakeholders and AI capabilities. Dissect requests from departments like Finance or Legal and re-engineer their processes for compatibility with LLMs and agents. Systems Integration & Orchestration: Integrate agents with Salesforce, NetSuite, Workday, Marketo, Vena, and other enterprise platforms via APIs and webhooks. Integrate agents with Salesforce, NetSuite, Workday, Marketo, Vena, and other enterprise platforms via APIs and webhooks. Data Strategy for Agents: Extract, clean, and contextually feed data from siloed systems into AI models for accurate outputs. Extract, clean, and contextually feed data from siloed systems into AI models for accurate outputs. Influence Internal AI Strategy: Identify high-value use cases for multi-step agentic automation. Identify high-value use cases for multi-step agentic automation. Governance & Maintenance: Own the lifecycle of internal agents, ensuring security, cost-effectiveness, and functionality. Own the lifecycle of internal agents, ensuring security, cost-effectiveness, and functionality.

We’re looking for a Creative Technologist who lives at the intersection of AI, automation, and creative problem-solving. This isn’t a “I use ChatGPT sometimes” role—we need someone who understands how large language models actually work, can architect end-to-end workflows, and knows when AI meaningfully saves time versus when it’s just a shiny distraction. You’ll work directly with the Head of Creative Technology to translate complex creative needs into repeatable, scalable systems. Design, build, and maintain AI-powered workflows that support creative teams across the agency Advise on where AI meaningfully saves time or elevates craft—and push back when it doesn’t Translate creative briefs and production needs into repeatable, documented systems Prototype new tools and processes, test them in real workflows, and hand stable solutions off to production teams Scout and evaluate AI vendors, models, and platforms—separating signal from hype Run creative QA on AI-generated outputs to ensure quality and brand consistency Maintain clear documentation for every tool, workflow, and system you build Act as a buffer between leadership vision and day-to-day execution, keeping work moving and teams unblocked