Staff Engineer, Agentic Developer Platform

Panorama Education

1d•$233,750 - $343,750

About The Position

Panorama Education is a technology company helping K-12 school systems understand what students need and take effective action. By connecting academics, attendance, behavior, and engagement data with AI, we give educators a complete picture of each student and the intelligence to decide what to do next. We work with 2,000 districts and reach 15 million students nationwide, nearly 1 in every 4 students in the US. This scale means that the products we build help shape what's possible for educators and for learners. Panorama is at an inflection point with AI - we've heavily invested in it across our products and seen incredible market traction and now we're turning towards how we can use AI to transform how the company itself operates. As a Staff Engineer, Agentic Developer Platform, you'll design and own a new internal AI platform: the agent framework, skill infrastructure, and shared systems that every team at Panorama builds on. The goal is making it possible for engineers, PMs, and operators across the company to go from idea to working AI-powered workflow through core infrastructure. The work is greenfield. You'll make early architectural decisions that others will build on, create the tooling that turns experiments into things teams can actually rely on, and stay close enough to how people use what you build to keep improving it. This role is for someone who thinks in platforms, gets energy from enabling others, and wants what they build to matter, both inside the company and for the 15 million students we serve.

Requirements

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 Haves

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.

Responsibilities

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.

Benefits

annual bonuses or commission awards
equity awards
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