
201 - 500 employees
Founded 2012
đ Education
âď¸ SaaS
đ¤ Artificial Intelligence
đ° $60M Series C - Panorama Education on 2021-09
Education ⢠SaaS ⢠Artificial Intelligence
Panorama Education is an education technology company that provides an AI-powered SaaS platform for Kâ12 school districts to collect surveys, manage Multi-Tiered System of Supports (MTSS), track behavior and attendance, and personalize instruction and interventions. Its offeringsâlike Panorama Solara, Student Success (MTSS), and Surveys & Engagementâcombine data analytics, secure district-managed AI, and professional development to help educators identify needs, plan targeted actions, and measure student outcomes while maintaining privacy and compliance.
đ 2 days ago
đşđ¸ United States â Remote
đľ $233.8k - $343.8k / year
â° Full Time
đ´ Lead
đď¸ Platform Engineer
đŚ H1B Visa Sponsor
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201 - 500 employees
Founded 2012
đ Education
âď¸ SaaS
đ¤ Artificial Intelligence
đ° $60M Series C - Panorama Education on 2021-09
Education ⢠SaaS ⢠Artificial Intelligence
Panorama Education is an education technology company that provides an AI-powered SaaS platform for Kâ12 school districts to collect surveys, manage Multi-Tiered System of Supports (MTSS), track behavior and attendance, and personalize instruction and interventions. Its offeringsâlike Panorama Solara, Student Success (MTSS), and Surveys & Engagementâcombine data analytics, secure district-managed AI, and professional development to help educators identify needs, plan targeted actions, and measure student outcomes while maintaining privacy and compliance.
⢠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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