Manager, Software Engineering - Developer Experience

At ClickUp , we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible.

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)