Description
We are looking for a Senior Engineering Manager, Agents & Developer Productivity to lead teams building AI-assisted engineering workflows.
This role will partner with engineering leaders in infrastructure, developer productivity, security, product engineering, ads, machine learning, and senior technical leadership to make AI-native development safe, measurable, scalable, and effective across Reddit.
Reddit is entering a new era of software development. AI and agentic workflows are changing how code is planned, written, reviewed, tested, and shipped. Used well, these tools can help engineers move faster with higher confidence. Used poorly, they can increase complexity, review burden, operational risk, and cost.
This role will help Reddit build the path for agentic development: flexible enough for teams to experiment, opinionated enough to protect quality, and measurable enough to prove whether AI is increasing delivered value rather than just activity.
Just as importantly, this role will help bring teams along for that change, building trust in new workflows and helping Reddit evolve how we build software together.
Responsibilities
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.
- 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.
Requirements
- 10+ years of experience across software engineering, engineering management, infrastructure, developer tools, platform engineering, AI tooling, or related technical domains, including experience leading engineering teams.
- Managed high-performing engineering teams delivering large-scale technical platforms or infrastructure with executive visibility and cross-functional dependencies.
- Deep fluency in developer productivity systems: CI/CD, code review, testing, deployment, observability, developer environments, internal platforms, or AI-assisted development workflows.
- Understand AI and LLM-enabled software development beyond personal productivity. Can reason about agent workflows, governance, evals, telemetry, code quality, review burden, cost controls, and operational risk.
- Highly data-driven, but skeptical of shallow metrics. Know how to build measurement systems that connect workflow improvements to business and engineering outcomes.
- Exceptional cross-functional leader and communicator. 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.
- Led meaningful organizational change, not just technical delivery. 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.
- Care deeply about engineering quality, operational excellence, and developer experience, and know how to balance speed with long-term maintainability and reliability.
- Get things done. Not satisfied with status reporting, process theater, or activity metrics. Deliver outcomes and help teams understand the difference between motion and impact.
- Pragmatic and iterative. Can introduce structure without slowing teams