[Hiring] Senior Full Stack Developer @FullBloom

Role Description

This role is hired to build agent-first. The Senior Full-Stack Developer, Agentic Engineer is a founding member of a small, new engineering squad building the next generation of staff-facing applications, APIs, and data services for Catapult Learning and SESI Schools. The squad starts with net-new functionality and then rebuilds existing capabilities one at a time. It is not responsible for maintaining the current legacy systems. AI coding agents are the default way this engineer plans, implements, tests, and refactors, and they own features end to end, from requirements and technical design through prototype, production code, and deployment.

Just as important, they help shape how the new squad works. FullBloom is moving its engineering teams from ad hoc AI-assisted coding to a shared agentic build pipeline. That pipeline runs on agent-ready specs, AI-driven implementation and testing, automated quality gates, and human review of everything that ships. This engineer helps stand that pipeline up, codifies what works, and mentors the early-career engineers who join as the squad grows. Because agents make code cheap to write, the hardest parts of this job are deciding what to build, defining it clearly, and judging whether what the agents produced is right.

They work with product, design, QA, DevOps, and security partners, often directly from lightweight product briefs. They move faster without compromising student data protection, accessibility, or SOC 2 controls.

Qualifications

• Bachelor's degree in Computer Science or a related field and 5+ years of relevant work experience, or equivalent experience
• Daily, hands-on use of AI coding agents (e.g., Claude Code, Codex, Cursor) to build production software, with concrete examples of how it changed your workflow and results
• Working knowledge of what makes agents effective: context management, task decomposition, clear specs and prompts, and rigorous verification of agent output
• Demonstrated judgment reviewing agent output: you can find and explain problems in code that looks correct, and you know when a change needs closer human review
• Strong product sense: you can turn an ambiguous problem into a clear, well-scoped spec and push back on features that aren't needed
• Strong proficiency in TypeScript/JavaScript, React, HTML, and CSS
• Experience designing and building REST APIs and back-end services
• Strong working knowledge of relational databases (e.g., PostgreSQL), including data modeling, query writing, and performance tuning
• Experience with cloud hosting, serverless technologies, and infrastructure as code (e.g., AWS with Terraform or CDK)
• Proficiency in writing and automating unit, integration, and end-to-end tests
• Experience with CI/CD pipelines such as GitHub Actions, and owning your own environments and deployments
• Git and version control expertise, including pull-request-based review workflows
• Strong knowledge of cross-browser, cross-platform, and cross-device compatibility
• Experience building accessible applications that meet WCAG and ADA requirements
• A track record of owning features end to end and mentoring other engineers
• Clear written communication, including specs and technical designs that both people and agents can work from
• Comfort working in short, experimental cycles and discarding work that doesn't pan out

Requirements

• Agentic engineering and delivery
• Agent-first development: Use AI coding agents (e.g., Claude Code) as the primary way to plan, implement, test, and refactor code, while staying accountable for every line that ships
• Judging agent output: Treat review and validation as the core of the job; catch defects in code that looks correct, decide which changes need closer human review, and own what merges
• Requirements to architecture: Work directly with product partners and stakeholders to turn lightweight briefs into agent-ready specs, technical designs, data flows, and acceptance criteria
• Proof-of-concept development: Prototype quickly to test feasibility, show value, and settle design questions before committing to a full build
• Short, experimental cycles: Build end-to-end working skeletons first (Simple, Lovable, Complete), apply YAGNI to keep AI-generated scope in check, and throw away work that doesn't earn its place
• Full-stack ownership: Own features end to end across front-end interfaces, APIs, back-end services, data stores, infrastructure as code, and deployment pipelines
• Integrations and data services: Build and maintain integrations with identity providers, rostering and student information systems, internal APIs, and shared data platforms
• Rebuilding legacy capabilities: Use agents to read existing systems well enough to extract their business rules, data, and edge cases, then rebuild those capabilities one at a time on the new stack
• Building the agentic pipeline
• Context and standards: Write and maintain the artifacts agents work from, including repository instruction files (CLAUDE.md/AGENTS.md), AI-readable coding standards, architecture decision records, and a shared skills and prompt library
• Tooling and integrations: Configure agent tooling, permissions, and MCP integrations (e.g., GitHub, Jira, browser automation) so agents can work safely in our repositories and environments
• Environments and DevOps: Own the squad's DevOps alongside the code, including preview environments, safe test data with student information removed, and deployment pipelines, so agents can build and verify changes without touching production data
• Quality gates: Strengthen automated tests, CI checks, and AI-assisted code review so agent output meets the same bar as human code before it merges
• Verification loops: Build feedback loops that let agents check their own work, such as end-to-end tests and headless browser checks, while keeping independently written tests for high-risk areas
• Governance: Apply guardrails for secrets, data access, and student data, and keep agent-driven work aligned with SOC 2 change-management and SDLC controls
• Growing the squad
• Mentoring: Mentor early-career engineers who join the squad, with a focus on the fundamentals and review habits that agents don't teach
• Codifying practice: Turn what works into repeatable playbooks, templates, and skills that new squad members and FullBloom's other engineering teams can reuse, and log friction points so the pipeline improves with each project
• Working across roles: Work closely with product and design partners in a model where roles blur, including designers submitting changes against working code
• Measuring outcomes: Track delivery and quality signals such as lead time, rework, escaped defects, and change-failure rate, not tool adoption alone
• Quality, reliability, and collaboration
• Review human- and agent-written code for correctness, security, maintainability, and performance
• Monitor, troubleshoot, and resolve production issues, and take part in incident response and post-incident reviews
• Communicate scope, timelines, and trade-offs clearly to technical and non-technical colleagues
• Promote usability, accessibility, and security best practices
• Other duties as assigned

Benefits

• Remote work setting

Posted Salary Range

USD $120,000.00 - USD $150,000.00 /Yr.