Sparkrock
Software
AI-NativeSoftwareEngineeringDirector
Neural analysis suggests this role is
optimal for Director candidates.
“AI-Native Software Engineering Director at Sparkrock. Skills: AI-Native Engineering, Software Engineering, Quality Engineering, Experimentation. Design, execute, and measure AI-Native software development and. Identify engineering bottlenecks where AI-Native workflows can improve”
What You'll Achieve.
Achieve measurably better outcomes through AI-Native ways of working; Unlock materially higher levels of productivity, quality, and innovation; Improve productivity, quality, speed, developer experience, or release confidence; Improve test automation, regression prevention, validation, code review, quality gates, and production readiness; Drive organization-wide adoption of proven AI-Native engineering practices
Industry & Context.
Identify engineering bottlenecks; Analyze experiment results; Troubleshooting
What They're Looking For.
Must Have
Bachelor's degree or higher in Computer Science, Computer Engineering, Software Engineering, or related field, or equivalent practical experience, 8+ years of hands-on software engineering experience delivering production software systems, Strong hands-on software engineering background with experience in modern software development practices and production-grade systems, Practical experience using AI-assisted development tools, coding assistants, coding agents, AI-enabled IDEs, AI-powered testing, AI-supported code review, or agentic software development workflows in real engineering environments, Experience evaluating and rolling out AI engineering tools, coding agents, test generation tools, code review assistants, documentation assistants, or developer productivity platforms, Experience leading engineering transformation, engineering excellence, developer productivity, quality engineering, platform engineering, technical enablement, or software development process improvement initiatives, Experience designing, executing, measuring, and scaling experiments that improve engineering productivity, quality, developer experience, or delivery outcomes, Experience improving engineering outcomes through process innovation, tooling adoption, productivity initiatives, quality engineering improvements, or organizational decisions across all levels of the engineering organization, from individual contributors to executives, Ability to separate durable engineering value from short-lived AI hype
Nice to Have
Experience building or scaling AI-Native engineering practices across multiple teams, Experience leading developer productivity, engineering excellence, platform engineering, quality engineering, DevOps transformation, or technical enablement initiatives, Experience implementing engineering metrics, productivity dashboards, developer experience measurement, or value-stream improvement frameworks, Experience defining responsible AI usage standards, AI-generated code review practices, security guardrails, or enterprise AI tooling policies, Experience with large-scale distributed, remote, or global engineering organizations, Experience with enterprise SaaS, ERP systems, public sector, education, nonprofit, or mission-critical business applications, Experience modernizing legacy systems or improving productivity in complex enterprise codebases using AI-assisted workflows
What You'll Do.
and measure AI-Native software development and
Identify engineering bottlenecks where AI-Native workflows can improve
Evaluate emerging AI engineering tools
Develop and institutionalize AI-Native development
Define and maintain engineering quality bars
Create AI-Native quality engineering practices that improve test
Establish balanced metrics and measurement frameworks for engineering
Analyze experiment results and recommend whether practices should
and enablement materials
Coach engineers and engineering leaders to maximize effectiveness
Drive organization-wide adoption of proven AI-Native engineering practices
Define safe and responsible practices for AI-generated code
Partner with engineering
Continuously improve software development
Develop strategic recommendations for the future evolution of
How You'll Work.
Team & Collaboration
Engineering teams; Engineering leaders; Cross-functional teams; Product; QA; Security; DevOps; Platform; Executive leadership
Communication Scope
Coaching; Enablement; Influence; Feedback loops
Process & Methodology
Roadmap execution, Release management
Full Job Description
## Description Software engineering is undergoing its biggest transformation since Agile, Cloud, and DevOps. AI is changing how software is designed, built, tested, reviewed, documented, and delivered. The organizations that learn how to turn this shift into disciplined engineering practice will create a meaningful advantage in speed, quality, innovation, and talent leverage. At Sparkrock, we help social benefit organizations—such as nonprofits, school boards, and government agencies—operate more effectively. Every day, tens of thousands of users rely on our platforms to manage critical financial and administrative workflows. Sparkrock is looking for an AI-Native Software Engineering Director to lead that transformation. This is not a traditional software engineering leadership role focused on roadmap execution, release management, or managing a large reporting line. Your mission is to build the AI-Native Engineering Operating System for Sparkrock: the experiments, workflows, standards, metrics, guardrails, playbooks, and coaching systems that define how our engineering teams build software in the AI era. You will design and run experiments across software development and quality engineering, evaluate emerging AI engineering tools and agentic workflows, establish AI-Native development and QA standards, and coach engineers and engineering leaders to unlock materially higher levels of productivity, quality, and innovation. This role offers a unique opportunity to shape the future of software engineering within a global, fully remote organization. You will directly influence how engineering teams use AI-assisted development, coding agents, quality automation, and human-AI collaboration to build exceptional software safely, reliably, and at scale. Success in this role is measured by your ability to help engineering teams achieve measurably better outcomes through AI-Native ways of working, not by the size of the team you manage or the number of tools you introduce. If y
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