Manager, Software Engineering - Developer Experience

Role Overview

As the Engineering Manager - Developer Productivity, you will lead a high-impact team focused on improving developer workflows, reducing friction, and enhancing the overall engineering experience. You’ll collaborate across teams to identify bottlenecks, implement scalable solutions, and foster a culture of continuous improvement. Your work will directly influence the speed, quality, and happiness of our engineering organization.

Responsibilities

• Infrastructure & Tooling
• Oversee the development and maintenance of CI/CD pipelines, build systems, and internal tools, including AI-powered internal tooling and agents.
• Ensure our developer infrastructure is scalable, reliable, and secure.
• Evaluate and implement new AI-native technologies, considering token economics, compute costs, and AI spend alongside productivity gains.
• Team Leadership & AI People Development
• Build, mentor, and lead a team of engineers focused on developer productivity.
• Foster a culture of collaboration, innovation, and accountability.
• Set clear goals and provide regular feedback to drive team performance in partnership with your Director.
• Assess each report's AI fluency level (Assisted, Augmented, Native) and set individual growth targets.
• Include AI fluency as a coaching topic in 1:1s; pair less AI-fluent engineers with AI-native peers for knowledge transfer.
• Create space for AI experimentation: dedicated time, safe-to-fail projects, and learning sprints.
• Recognize and reward AI-driven improvements in performance conversations.
• Collaboration & Communication
• Work closely with engineering, product, and design teams to align priorities.
• Advocate for developer needs and ensure alignment with company goals.
• Communicate capabilities and limitations to non-technical stakeholders.
• Write specs, rules files, and documentation that make AI more effective for the whole team.
• Metrics & Continuous Improvement
• Define and track key metrics to measure developer productivity, satisfaction, and AI-driven productivity gains.
• Use data-driven insights to prioritize initiatives and demonstrate impact.
• Track team-level AI adoption metrics and report on AI-augmented workflow effectiveness.
• Continuously iterate on processes to improve engineering velocity and quality.
• AI-Native Developer Experience
• Identify and eliminate pain points in the development lifecycle, with a focus on AI-augmented workflows.
• Drive adoption of AI coding tools (Cursor, Brain, copilots) as the default across engineering teams.
• Ensure every team member has access to and is actively using AI tools; track adoption and remove blockers (tooling, access, training).
• Partner with engineering teams to design AI-augmented development workflows that multiply team velocity.
• Coach engineers on the Builder model: planning, delegating to agents, reviewing with judgment, and shipping with velocity.
• AI Governance & Risk
• Ensure the team follows AI usage guidelines (data handling, code review, IP considerations).
• Flag risks from AI-generated code (security, correctness, licensing) proactively.
• Maintain visibility into what AI tools the team is using and how they're being used.
• Run regular retros and feedback loops on AI-related outcomes.
• Review AI-generated outputs alongside the team to build shared quality standards.

Qualifications

• Experience: 7+ years in software engineering, with 3+ years in a leadership role managing engineering teams.
• Technical Expertise: Strong understanding of developer tools, CI/CD pipelines, and modern software development practices. Hands-on experience with AI coding tools (Cursor, Claude Code, Codex, or similar) and an understanding of how to integrate them into engineering workflows.
• AI Fluency: Demonstrated ability to operate at the AI Augmented level or above: you personally use AI tools daily and have coached others on effective AI-assisted development. Familiar with prompt engineering, context engineering, and token economics.
• Builder Mindset: Comfortable with the 80/20 model (planning and review vs. direct execution). Experience orchestrating work across humans and AI agents, with strong judgment on when to delegate to AI vs. when human decision-making is critical.
• Leadership Skills: Proven ability to build and lead high-performing teams. Experience assessing and developing AI fluency across a team.
• Problem-Solving: Track record of identifying inefficiencies and implementing scalable solutions, including building or adopting AI-powered tooling and eval frameworks.
• Governance Awareness: Understanding of AI & non-AI related risks (security, correctness, licensing, data handling) and experience establishing quality standards for outputs.
• Collaboration: Exceptional communication and stakeholder management skills. Ability to translate AI capabilities and limitations to non-technical audiences.
• Mindset: Passion for improving developer experiences and driving organizational impact through AI-native practices with a customer first mentality.