Original listing text, shown exactly as published by the company.
What You'll Do
You will lead teams across Reddit's Developer Productivity and AI portfolio, spanning agent infrastructure, AI-assisted code review, developer environments, LLM governance, developer tooling, AI workflow measurement, and software delivery automation. You will partner deeply with engineering teams as a technical leader, balancing execution, organizational alignment, technical quality, and long-term platform strategy.
In this role, you will
- Lead and grow high-performing engineering teams focused on AI-native developer productivity, fostering a culture of technical excellence, ownership, inclusion, and continuous improvement.
- Partner with Developer Productivity and Infrastructure leaders to translate strategy into scalable platforms and engineering roadmaps across code review, agent infrastructure, self-service LLM access, evaluation and observability systems, development environments, and safe agent workflows across the software development lifecycle.
- Define and evolve the operating model for AI-native software development, including paved paths, platform adoption strategies, governance models, and engineering standards that enable safe and effective experimentation at scale.
- Drive alignment across infrastructure, developer experience, reliability, security, compliance, product engineering, machine learning, and executive stakeholders. You will influence across organizations and create clarity across teams with different priorities and incentives.
- Build engineering systems and organizational processes that help teams move from experimentation to durable adoption of AI-assisted workflows while maintaining quality, reliability, and operational safety.
- Develop communication and enablement mechanisms that bring engineers and leaders along for the journey, including technical strategy reviews, working groups, training, documentation, rollout plans, and clear narratives that connect workflow change to business and engineering outcomes.
- Build and operationalize measurement systems that distinguish real productivity from vanity metrics. Partner with engineering and data teams to define metrics such as design-to-test time, test-to-launch time, review turnaround, change failure rate, time to restore service, AI-authored code quality, AI-assisted pull request load, migration throughput, and developer satisfaction.
- Drive AI governance and platform adoption in a way that preserves engineering creativity while ensuring security, cost management, privacy, compliance, and operational safety are built into the development lifecycle.
- Use technical judgment to challenge assumptions, identify systemic risks, guide architectural tradeoffs, and help teams make pragmatic decisions across AI tooling, CI/CD, code review, deploy automation, developer environments, and production reliability.
- Partner with senior engineering leadership on organizational planning, technical investment strategy, hiring, career development, and long-term platform evolution.
Who You Are
- You have 14+ years of experience across software engineering, engineering management, infrastructure, developer tools, platform engineering, AI tooling, or related technical domains, including experience leading engineering teams.
- You have managed high-performing engineering teams delivering large-scale technical platforms or infrastructure with executive visibility and cross-functional dependencies. You are comfortable operating with senior leaders while still engaging deeply in technical and operational details.
- You have deep fluency in developer productivity systems: CI/CD, code review, testing, deployment, observability, developer environments, internal platforms, or AI-assisted development workflows.
- You understand AI and LLM-enabled software development beyond personal productivity. You can reason about agent workflows, governance, evals, telemetry, code quality, review burden, cost controls, and operational risk.
- You are highly data-driven, but skeptical of shallow metrics. You know how to build measurement systems that connect workflow improvements to business and engineering outcomes.
- You are an exceptional cross-functional leader and communicator. You bring people together, create clarity across organizations, build trust across functions, and translate complex technical and organizational change into clear narratives for engineers, managers, executives, and cross-functional partners.
- You have led meaningful organizational change, not just technical delivery. You know how to shift behavior across teams by listening to concerns, building confidence, and making the desired path easier and more valuable than the status quo.
- You care deeply about engineering quality, operational excellence, and developer experience, and you know how to balance speed with long-term maintainability and reliability.
- You get things done. You are not satisfied with status reporting, process theater, or activity metrics. You deliver outcomes and help teams understand the difference between motion and impact.
- You are pragmatic and iterative. You can introduce structure without slowing teams down, and you know when a paved path should be adopted through value rather than enforcement.
Preferred Qualifications
- Experience leading developer productivity, platform engineering, infrastructure modernization, or AI tooling teams at large scale.
- Experience with LLM platforms, AI-assisted development tools, agentic workflows, or AI governance in an enterprise engineering environment.
- Experience building internal platforms or developer tools where success depends on trust, usability, reliability, and measurable value.
- Experience leading organizational transformation, platform adoption, or engineering workflow modernization initiatives across large engineering organizations.
- Familiarity with engineering productivity frameworks such as DORA, SPACE, or similar multi-dimensional measurement approaches.
- Experience working in fast-moving environments where technical strategy, organizational design, and execution mechanisms are evolving at the same time.
