As a Machine Learning Engineer, you’re a highly motivated individual with strong fundamentals in computer science and hands-on experience across the full model development lifecycle. Develop and deliver end-to-end machine learning solutions, including defining technical requirements, architecting scalable systems, and implementing monitoring, logging, and maintenance workflows. Collaborate closely with engineers, product managers, clinicians, and cross-functional partners to build new ML products and enhance existing systems. Lead the design and implementation of MLOps frameworks, including pipeline development, CI/CD integration, drift detection, retraining workflows, and rollback strategies. Monitor model performance in production, identify issues, propose remediation steps, and ensure strong test coverage and system reliability. Utilize contemporary software engineering practices to implement scalable, secure, and maintainable AI/ML systems. Develop and customize API integrations to enable seamless connectivity between cloud-based systems and ML services. Participate in architectural discussions to ensure ML platforms meet compliance, performance, and scalability standards.

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.