
201 - 500 Mitarbeiter
Gegründet 2012
📚 Bildung
☁️ SaaS
🤖 Künstliche Intelligenz
💰 €60.000.000 Series C - Panorama Education im 2021-09
Education • SaaS • Artificial Intelligence
Panorama Education ist ein Bildungstechnologieunternehmen, das eine KI-gestützte SaaS-Plattform für Schulbezirke der Klassen K–12 bereitstellt, um Umfragen zu sammeln, ein Multi-Tiered System of Supports (MTSS) zu verwalten, Verhalten und Anwesenheit zu verfolgen und Unterricht sowie Interventionen zu personalisieren. Die Angebote — wie Panorama Solara, Student Success (MTSS) und Surveys & Engagement — kombinieren Datenanalyse, sichere bezirksverwaltete KI und berufliche Weiterbildung, um Lehrkräften dabei zu helfen, Bedürfnisse zu identifizieren, gezielte Maßnahmen zu planen und Schülerergebnisse zu messen, während Datenschutz und Konformität gewahrt bleiben.
🕒 vor 1 Monat
🇺🇸 Vereinigte Staaten – Remote
💵 $233.750 - $343.750 / Jahr
⏰ Vollzeit
🔴 Experte
🏗️ Plattformingenieur
🦅 H1B-Visum-Sponsor
🗣️🇺🇸🇬🇧 Englisch erforderlich
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201 - 500 Mitarbeiter
Gegründet 2012
📚 Bildung
☁️ SaaS
🤖 Künstliche Intelligenz
💰 €60.000.000 Series C - Panorama Education im 2021-09
Education • SaaS • Artificial Intelligence
Panorama Education ist ein Bildungstechnologieunternehmen, das eine KI-gestützte SaaS-Plattform für Schulbezirke der Klassen K–12 bereitstellt, um Umfragen zu sammeln, ein Multi-Tiered System of Supports (MTSS) zu verwalten, Verhalten und Anwesenheit zu verfolgen und Unterricht sowie Interventionen zu personalisieren. Die Angebote — wie Panorama Solara, Student Success (MTSS) und Surveys & Engagement — kombinieren Datenanalyse, sichere bezirksverwaltete KI und berufliche Weiterbildung, um Lehrkräften dabei zu helfen, Bedürfnisse zu identifizieren, gezielte Maßnahmen zu planen und Schülerergebnisse zu messen, während Datenschutz und Konformität gewahrt bleiben.
• Design and own the company's core AI infrastructure: the shared systems, integration patterns, and runtime environments that all AI-powered work is built on top of. • Make the foundational decisions that others will depend on, including model selection and abstraction, orchestration patterns, data access layers, and deployment standards. • Design the platform so teams can extend it on their own, without needing your involvement for every new use case. • Build the operational layer teams need to trust what they've deployed: logging, evals, cost tracking, latency monitoring, and feedback loops. • Design and build the company's agent framework and skill library, the reusable building blocks that teams reach for when automating workflows, connecting systems, or extending AI capabilities into new areas. • Define the interfaces, contracts, and composition patterns that let squads build new agents and skills confidently without reinventing core infrastructure. • Ensure the framework supports a range of complexities, from simple single-step automations to multi-agent workflows spanning systems and teams. • Help internal teams go from 'we have an idea' to 'we have a working implementation' by providing the technical scaffolding, guidance, and support. • Build the internal tooling, documentation, and onboarding paths that make the platform genuinely accessible to team members across the company. • Create abstractions that lower the floor for AI development without boxing in the complex cases. • Act as a technical partner to teams adopting the platform, helping them get unblocked, apply patterns correctly, and avoid pitfalls early. • Partner with Product to surface where AI capabilities can remove friction, accelerate workflows, or unlock things internal teams don't yet know are possible. • Maintain a feedback loop with internal customers so the platform evolves around how people actually work, not how the roadmap assumed they would. • Define how the company evaluates, adopts, and evolves AI capabilities responsibly, establishing standards for safety, reliability, and quality that hold across teams. • Partner with engineering leadership to align AI infrastructure with broader architectural direction and long-term system health. • Contribute to org-wide technical discussions, bringing a platform and infrastructure lens to decisions that affect how AI work gets done across the company.
• 8+ years of professional software engineering experience, with meaningful depth in platform, infrastructure, or developer tooling. • Experience building shared systems that other engineers build on, and an intuition for what makes internal platforms succeed or stall. • Hands-on experience with production AI systems (LLM integrations, tool-using agents, retrieval pipelines, or comparable work), and a track record of enabling other builders to work with those systems confidently. • Strong instincts for API and abstraction design, knowing how to expose the right surface area and hide the right complexity. • Familiarity with MCP or similar tool-use and integration patterns. • A track record of scoping and delivering greenfield technical work, including making early architectural decisions that hold up over time. • Collaborative and transparent by default, with the ability to lead cross-functional alignment without formal authority. • Clear-eyed about the gap between AI that works in demos and AI that works in production, and experienced in closing it. • Actively seeks out product and cross-functional context, energized by the opportunity to push the work forward across teams, not just within engineering. • Nice to Have: Experience with multi-agent orchestration frameworks. • Background in internal developer platforms, enablement engineering, or technical program leadership. • Experience in defining and rolling out engineering standards across a multi-team organization.
• 401K with an employer match • Health, dental, vision, life insurance, and short-term and long-term disability coverage. • Flexible spending account for health care and dependent care • Wellness Reimbursement • Work from Home Reimbursement • Flexible vacation policy • Parental leave program • Company Issued Laptop
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