AI-Native Software Engineering Director
New
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SparkrockEnterprise Software, SaaS
This role is open to applicants from any country. We hire globally., We work asynchronouslyFull-TimeDirector
SalaryOTE USD: $100,000; Base: $90,000; Variable: 10%
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Job Details
- Experience
- 8+ years of hands-on software engineering experience; 4+ years of leadership/management experience
- Required Skills
- CI/CDDevOpsProcess improvementSoftware EngineeringChange Management
Requirements
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
- 8+ years of hands-on experience delivering production-grade software systems.
- 4+ years of experience in software engineering leadership or management.
- Practical experience using AI-assisted tools, coding agents, and AI-enabled IDEs in real engineering environments.
- Experience evaluating and rolling out AI development tools, coding agents, or developer productivity platforms.
- Experience leading engineering transformation, platform engineering, or quality engineering initiatives.
- Proven ability to design and scale experiments that improve engineering outcomes.
- Strong understanding of modern software engineering, CI/CD, DevOps, cloud-native development, and security.
- Ability to design human-AI workflows that maintain quality, reliability, and human accountability.
- Strong analytical and data-driven decision-making skills with the ability to define meaningful metrics.
- Exceptional coaching, mentoring, and change leadership abilities.
Responsibilities
- Design, execute, and measure AI-Native software development and quality engineering experiments.
- Evaluate emerging AI engineering tools, coding agents, and developer productivity platforms.
- Develop and institutionalize AI-Native practices for development, testing, review, and delivery.
- Define engineering quality bars, usage guardrails, and workflow templates for AI-assisted development.
- Establish metrics and measurement frameworks for engineering productivity, quality, and developer experience.
- Coach engineers and leaders to maximize effectiveness through human-AI collaboration.
- Create playbooks and enablement materials to scale successful practices across the organization.
- Define responsible usage standards for AI-generated code, security, and data protection.
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