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.
- 4+ years of software engineering leadership/management experience, directly managing and growing engineering teams.
- 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
- 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!
Work Arrangement
Remote (Worldwide) — Any country
