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 development, Quality engineering, Experimentation. Design, execute, and measure AI-Native software development experiments. Design, execute, and measure AI-Native quality engineering 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; Improve productivity; Improve quality; Improve speed; Improve developer experience; Improve release confidence; Improve test automation; Improve regression prevention; Improve validation; Improve code review; Improve quality gates; Improve production readiness; Achieve breakthrough performance through AI-Native practices
Industry & Context.
Root cause analysis; Debugging; Troubleshooting
What They're Looking For.
Must Have
Bachelor's degree or higher in CS, CE, SE, or related field, or equivalent practical experience, 8+ years hands-on software engineering experience, Strong hands-on software engineering background, Practical experience using AI-assisted development tools, Experience evaluating and rolling out AI engineering tools, Experience leading engineering transformation initiatives, Experience designing, executing, measuring, and scaling experiments, Experience improving engineering outcomes through process innovation
Nice to Have
Experience building or scaling AI-Native engineering practices, Experience implementing engineering metrics, Experience defining responsible AI usage standards, 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 using AI-assisted workflows
What You'll Do.
and measure AI-Native software development experiments
and measure AI-Native quality engineering experiments
Identify engineering bottlenecks for AI-Native workflow improvement
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 frameworks
Measure engineering productivity
Measure developer experience
Measure business impact
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 through AI-assisted development
Maximize effectiveness through agentic workflows
Maximize effectiveness through quality engineering
Maximize effectiveness through human-AI collaboration
Drive organization-wide adoption of AI-Native engineering practices
Enable AI-Native engineering practices
Measure AI-Native engineering practices adoption
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 through AI-Native approaches
Continuously improve QA processes through AI-Native approaches
Continuously improve automation processes through AI-Native approaches
Continuously improve CI/CD processes through AI-Native approaches
Continuously improve DevOps processes through AI-Native approaches
Continuously improve cloud engineering processes through AI-Native approaches
Continuously improve observability processes through AI-Native approaches
Continuously improve security processes through AI-Native approaches
Continuously improve delivery processes through AI-Native approaches
Develop strategic recommendations for software engineering evolution
How You'll Work.
Team & Collaboration
Engineering teams; Quality engineering teams; Cross-functional teams; Engineering leaders; Product; QA; Security; DevOps; Platform; Executive leadership
Communication Scope
Coaching
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
Applying for this AI-Native Software Engineering Director role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Lever
- Lever uses a streamlined one-page form — apply in under 5 minutes.
- LinkedIn import works well; review parsed data before submitting.
- The cover letter field is optional but visible to reviewers — use it to differentiate.
- Referral codes from employees can significantly boost visibility of your application.
ANONYMOUS · UNFILTERED
What do employees actually say about Sparkrock?
Real rants from real employees. Read before you apply.