Software Engineer III — Evaluation

GENERAL OVERVIEW: Education Advanced, Inc. is a rapidly growing educational technology (Ed-Tech) company in the K-12 industry. Our goal is to create operational software that empowers educators to work more efficiently in specific areas. Evaluation is our teacher and staff observation and evaluation product. It is a Ruby on Rails application hosted on AWS, and it includes AI-powered features that help educators do their best work. We are hiring a Software Engineer III to own significant parts of the product. This is a small, tight-knit team, so the role carries real breadth: you will work across the Rails application, our AI-powered features, the data integrations that connect Evaluation to other systems, and the cloud infrastructure it all runs on. You will also help others understand how the system works, so documentation and mentoring are part of the job, not something you get to after the sprint. You will start with a structured onboarding period, then help shape where the product and its architecture go next. This is a remote role with core availability during US Central Time business hours. We foster a supportive, inclusive, and innovation-driven culture where every voice matters and your work directly empowers teachers and students. Join us to grow, collaborate, and build solutions that transform K–12 education. QUALIFICATIONS: Roles and Responsibilities Design, build, ship, and support features across the Evaluation application, from the first product conversation through release and production support. Build and improve AI-powered product features: integrate large language models (LLMs) into real user workflows, and help design the prompts, safeguards, and quality checks that keep AI output accurate, safe, and useful for educators. Build and maintain data integrations between Evaluation and other systems, with a focus on reliability and data quality. Lead technical improvements on the roadmap, such as simplifying configuration, reducing technical debt, and modernizing parts of the application, and sequence that work so delivery keeps moving. Write clean, high-quality, testable, secure, and maintainable code, and help strengthen our automated testing practices. Build and operate in AWS using CI/CD and infrastructure as code, and troubleshoot production issues end to end across the application and infrastructure. Meet FERPA, state student-privacy, and WCAG accessibility requirements as part of normal delivery. Use AI-assisted development tools as a core part of how you work (planning, coding, testing, code review, and documentation) while checking everything they produce and standing behind every line you ship. Help shape how the team uses AI in the development lifecycle, including shared conventions, reusable prompts and instructions, and guardrails for safe use. Partner with Product Management to refine requirements, take part in backlog refinement and sprint planning, and turn specifications into scalable, secure implementations. Mentor teammates through code review, pairing, and technical planning, and write clear documentation that helps others work confidently in the system. Other duties as assigned. Qualifications and Education Requirements Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience. 5+ years building and operating production web applications, including recent, hands-on Ruby on Rails experience (Ruby, JavaScript, HTML, and CSS). Strong relational database skills (MySQL or similar): schema design, indexing, query performance, and safe migrations in a multi-tenant application. Hands-on experience writing automated tests in Rails (for example, RSpec) at the unit and integration level. Hands-on experience deploying, running, and debugging applications in AWS, including containers, CI/CD pipelines, and infrastructure as code. Solid grasp of secure web development (for example, OWASP Top 10, authentication and authorization, input validation, and secrets management). Daily, hands-on use of AI-assisted development tools in professional work (for example, GitHub Copilot, Claude Code, Cursor, or OpenAI Codex), beyond autocomplete to multi-step work such as building features, refactoring, writing tests, and exploring unfamiliar code. Skill at directing AI tools: giving clear specs and context, breaking work into small, checkable steps, and using tests and code review to catch output that is wrong, insecure, or made up. Sound judgment about AI and data safety, including what code and data should never be shared with an AI tool, especially student and educator data. A track record of owning ambiguous work end to end, from design through delivery and support, without needing it broken down for you. Comfort working productively in a mature codebase and improving it without stalling delivery. Excellent written and verbal communication for async, cross-time-zone teamwork, including explaining technical tradeoffs to non-engineers. Experience working in an Agile team (backlog refinement, sprint planning, stand-ups, and retrospectives). Core availability during US Central Time business hours. Authorized to work in the United States. Preferred Qualifications

Experience building LLM-powered features in production: prompt design, testing and evaluating AI output, cost and latency tradeoffs, and safeguards against unsafe or low-quality results. Experience with managed AI services from a major cloud provider. Experience building AI tools into a team's development process, such as shared repo instructions, reusable prompts, or custom agents that help AI tools follow team standards. Experience introducing or expanding automated testing in an existing codebase. Frontend modernization experience in a server-rendered Rails application (for example, Hotwire, Stimulus, or a component-based approach). Accessibility remediation experience against WCAG 2.1 AA. K-12 or public-sector software background, or experience with student information system (SIS) data and student data privacy. Experience mentoring engineers earlier in their career. How We Work

Small teams with real ownership: feature branches, a release branch model, frequent deploys, and feature flags for controlled rollout. AI across the whole lifecycle: We use AI tools throughout R&D, and we keep building them into how we plan, build, test, review, and release software. How we do that is still changing, and you'll help shape it. You own what you ship: if you can't explain code you submitted, it isn't ready, no matter what wrote it. Privacy first: student and educator data is handled with care in every tool and every environment.