Description
We are looking for a Principal Technical Program Manager, Developer Productivity to lead company-wide programs for 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 own and drive programs 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 peer, not from the stratosphere, but in the details, bringing programs to completion, balancing risk across systems and organizations, and ensuring execution stays aligned to Reddit's long-term developer productivity strategy.
In this role, you will:
- Partner with Developer Productivity and Infrastructure leaders to translate strategy into executable programs 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 drive the operating model for AI-native developer productivity, including how teams adopt paved paths, how guardrails are applied, how success is measured, and how risks are escalated or resolved.
- Create company-level alignment across infrastructure, developer experience, reliability, security, compliance, product engineering, machine learning, and executive stakeholders.
You will influence without authority and create clarity across teams with different incentives.
- Drive the social and cultural change required for AI-assisted development to succeed company-wide. You will build trust, address skepticism directly, create feedback loops, and help teams move from experimentation to durable adoption.
- Develop communication and enablement mechanisms that bring engineers and leaders along for the journey, including leadership forums, working groups, training, documentation, rollout plans, and clear narratives that connect workflow change to business and engineering outcomes.
- Build measurement systems that distinguish real productivity from vanity metrics. Partner with engineering and data teams to define industry-aligned 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 adoption in a way that preserves engineering creativity while ensuring security, cost management, privacy, compliance, and operational safety are not afterthoughts.
- Use technical judgment to challenge assumptions, identify systemic risks, and guide tradeoffs across AI tooling, CI/CD, code review, deploy automation, developer environments, and production reliability.
Requirements
- 14+ years of experience across technical program management, engineering, infrastructure, developer tools, platform engineering, AI tooling, or related technical domains.
- Led multi-year, multi-organization technical programs with executive visibility and ambiguous ownership.
- 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.
- Highly data-driven, but skeptical of shallow metrics.
- Exceptional cross-functional operator and communicator.
- Led meaningful organizational change, not just program execution.
- Pragmatic and iterative.
Preferred Qualifications
- Experience leading developer productivity, platform engineering, infrastructure modernization, or AI tooling programs at large scale.
- Experience with LLM platforms, AI-a