Sparkrock
Social benefit organizations
AI-NativeSoftwareEngineeringDirector
Neural analysis suggests this role is
optimal for Director candidates.
“AI-Native Software Engineering Director at Sparkrock. Skills: AI-Native Engineering, Software development, Quality engineering, Experimentation. Design AI-Native software development experiments. Execute AI-Native software development experiments”
What You'll Achieve.
Achieve measurably better outcomes through AI-Native ways of working; Unlock materially higher levels of productivity; Unlock materially higher levels of quality; Unlock materially higher levels of innovation
Industry & Context.
Root cause analysis; Debugging; 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.
Design AI-Native software development experiments
Execute AI-Native software development experiments
Measure AI-Native software development experiments
Design quality engineering experiments
Execute quality engineering experiments
Measure quality engineering experiments
Identify engineering bottlenecks
Improve productivity with AI-Native workflows
Improve quality with AI-Native workflows
Improve speed with AI-Native workflows
Improve developer experience with AI-Native workflows
Improve release confidence with AI-Native workflows
Evaluate emerging AI engineering tools
Evaluate coding agents
Evaluate AI-enabled development environments
Evaluate test generation tools
Evaluate code review assistants
Evaluate documentation tools
Evaluate developer productivity platforms
Develop AI-Native development practices
Institutionalize AI-Native development practices
Develop AI-Native testing practices
Institutionalize AI-Native testing practices
Develop AI-Native review practices
Institutionalize AI-Native review practices
Develop AI-Native documentation practices
Institutionalize AI-Native documentation practices
Develop AI-Native refactoring practices
Institutionalize AI-Native refactoring practices
Develop AI-Native debugging practices
Institutionalize AI-Native debugging practices
Develop AI-Native delivery practices
Institutionalize AI-Native delivery practices
Define engineering quality bars
Maintain engineering quality bars
Define operating standards
Maintain operating standards
Define usage guardrails
Maintain usage guardrails
Define workflow templates
Maintain workflow templates
Define best practices for AI-assisted software development
Maintain best practices for AI-assisted software development
Create AI-Native quality engineering practices
Improve test automation
Improve regression prevention
Improve quality gates
Improve production readiness
Establish balanced metrics
Establish measurement frameworks
Analyze experiment results
Recommend practice adoption
Recommend practice modification
Recommend practice scaling
Recommend practice retirement
Create operating models
Create enablement materials
Coach engineering leaders
Maximize effectiveness with AI-assisted development
Maximize effectiveness with agentic workflows
Maximize effectiveness with quality engineering
Maximize effectiveness with human-AI collaboration
Drive organization-wide adoption of AI-Native engineering practices
Enable AI-Native engineering practices
Influence AI-Native engineering practices
Measure AI-Native engineering practices
Provide continuous feedback on AI-Native engineering practices
Define safe practices for AI-generated code
Define responsible practices for AI-generated code
Define safe practices for AI-assisted testing
Define responsible practices for AI-assisted testing
Define safe practices for tool usage
Define responsible practices for tool usage
Define safe practices for data exposure
Define responsible practices for data exposure
Define safe practices for IP protection
Define responsible practices for IP protection
Define safe practices for security
Define responsible practices for security
Define safe practices for maintainability
Define responsible practices for maintainability
Define safe practices for human review
Define responsible practices for human review
Partner with engineering leadership
Partner with product leadership
Partner with QA leadership
Partner with security leadership
Partner with DevOps leadership
Partner with platform leadership
Partner with executive leadership
Align AI-Native transformation efforts with business priorities
Continuously improve software development processes with AI-Native approaches
Continuously improve QA processes with AI-Native approaches
Continuously improve automation processes with AI-Native approaches
Continuously improve CI/CD processes with AI-Native approaches
Continuously improve DevOps processes with AI-Native approaches
Continuously improve cloud engineering processes with AI-Native approaches
Continuously improve observability processes with AI-Native approaches
Continuously improve security processes with AI-Native approaches
Continuously improve delivery processes with AI-Native approaches
Develop strategic recommendations for software engineering evolution
How You'll Work.
Team & Collaboration
Engineering teams; Quality engineering; Product; QA; Security; DevOps; Platform; Executive leadership
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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