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 you are passionate about the future of engineering, energized by experimentation, serious about quality, and motivated by helping engineers achieve breakthrough performance through AI-Native practices, we would love to hear from you.
Responsibilities
- Design, execute, and measure AI-Native software development and quality engineering experiments.
- Identify engineering bottlenecks where AI-Native workflows can improve productivity, quality, speed, developer experience, or release confidence.
- Evaluate emerging AI engineering tools, coding agents, AI-enabled development environments, test generation tools, code review assistants, documentation tools, and developer productivity platforms.
- Develop and institutionalize AI-Native development, testing, review, documentation, refactoring, debugging, and delivery practices.
- Define and maintain engineering quality bars, operating standards, usage guardrails, workflow templates, and best practices for AI-assisted software development.
- Create AI-Native quality engineering practices that improve test automation, regression prevention, validation, code review, quality gates, and production readiness.
- Establish balanced metrics and measurement frameworks for engineering productivity, quality, cycle time, developer experience, adoption, and business impact.
- Analyze experiment results and recommend whether practices should be adopted, modified, scaled, or retired.
- Create playbooks, frameworks, operating models, and enablement materials that turn successful experiments into repeatable practices across the organization.
- Coach engineers and engineering leaders to maximize effectiveness through AI-assisted development, agentic workflows, quality engineering, and human-AI collaboration.
- Drive organization-wide adoption of proven AI-Native engineering practices through coaching, enablement, influence, measurement, and continuous feedback loops.
- Define safe and responsible practices for AI-generated code, AI-assisted testing, tool usage, data exposure, IP protection, security, maintainability, and human review.
- Partner with engineering, product, QA, security, DevOps, platform, and executive leadership to align AI-Native transformation efforts with business priorities.
- Continuously improve software development, QA, automation, CI/CD, DevOps, cloud engineering, observability, security, and delivery processes through AI-Native approaches.
- Develop strategic recommendations for the future evolution of software engineering at Sparkrock.
Requirements
- Bachelor's degree or higher in Computer Science, Computer Engineering, Software Engineering, or a 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 transformation.
- Experience driving the adoption of new engineering practices across multiple teams or organizations.
- Experience coaching engineers and engineering leaders through meaningful changes in engineering practices, tools, workflows, or operating models.
- Experience establishing engineering standards, quality bars, operating procedures, usage guardrails, quality frameworks, or operational excellence programs.
- Strong understanding of modern software engineering, software quality engineering, testing strategies, automation, CI/CD, DevOps, cloud-native development, observability, security, and developer productivity practices.
- Ability to design human-AI workflows that improve engineering outcomes while preserving quality, maintainability, security, reliability, and human accountability.
- Strong analytical and data-driven decision-making capabilities, including the ability to define meaningful metrics, establish baselines, interpret results, and avoid vanity metrics.
- Strong systems-thinking mindset with the ability to optimize complex human, technical, and organizational systems.
- Exceptional coaching, mentoring, facilitation, and change leadership skills.
- Excellent written, verbal, and presentation communication skills.
- Ability to influence technical and 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.
Benefits
We don't call them perks; they're part of what makes working at Sparkrock great.
- Access to leading AI engineering tools, platforms, and technologies, with the freedom to experiment, evaluate, and shape how they are adopted across the organization.
- A unique opportunity to define AI-Native engineering practices for a mission-driven enterprise software company.
- We are 100% remote and global. Live your best life wherever that may be, and never lose out on career opportunities because of it.
- Flexible work hours. We work asynchronously and don’t care when you’re online, just that you deliver great results and are there for our customers.
- We are dedicated to your growth with consistent and meaningful feedback, support in achieving your personal career goals, and access to leading-edge tools, playbooks, and technology to amplify your experience.
- Introductions to thought leaders in the space and webinars on cutting-edge tech hot topics.
- Stipend to help set up your ideal home office.
- Focus on culture: coffee chats, happy hours, cooking classes, book clubs, and more!
We strive to build a team that reflects the diversity of the communities we serve. We encourage applications from traditionally underrepresented groups, including women, visible minorities, Indigenous peoples, people identifying as LGBTQ2SI, veterans, and people with disabilities.
If you are excited by the opportunity to help define the future of software engineering in the AI era, we encourage you to apply.
All open roles are for existing vacancies unless otherwise communicated to the candidate. We are committed to keeping candidates informed throughout the process and will notify all interviewed applicants of our hiring decision within 45 days of their interview. Sparkrock retains all job postings and related recruitment information for a minimum of three years.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
