Program Management Fellow

The Washington Post•DC-Washington-TWP Headquarters, DC

10d•$159,100 - $265,100•Onsite

About The Position

The Washington Post is seeking a Program Management Fellow to define, implement, and continuously improve the operating processes that enable Product and Engineering teams to plan, build, and ship their work. This senior program-management leadership role focuses on the practices, tools, decision rights, and operating rhythms that move work from strategy and roadmap planning through design, development, launch, and learning. Working closely with Product and Engineering leadership, the Fellow will identify sources of friction, improve delivery discipline and visibility, and help translate roadmap priorities into executable plans while preserving Product Management’s ownership of what is built and why. A central focus is advancing an AI-enabled development lifecycle by identifying and embedding effective uses of AI across planning, development, testing, documentation, and operational work. The position is based in Washington, D.C. The Washington Post’s office-based work is on-site five days a week, with certain exceptions for newsgathering and business travel.

Requirements

Significant experience in program management, technical program management, product operations, engineering operations, or a related discipline within complex product and technology organizations.
Demonstrated experience leading large, complex, cross-functional programs involving Product, Engineering, Design, Data, and business stakeholders.
Experience designing, implementing, or materially improving product-development or engineering operating processes at organizational scale.
Strong understanding of modern product and software development lifecycles, including planning, scoping, sequencing, dependency management, development, testing, launch, and continuous improvement.
Demonstrated ability to translate strategic priorities and roadmaps into execution plans, milestones, ownership, dependencies, and decision points.
Experience identifying systemic delivery bottlenecks and root causes and leading process improvements that measurably improve execution.
Experience establishing and using delivery metrics such as predictability, cycle time, throughput, and quality, or comparable measures, to evaluate organizational effectiveness.
Strong cross-functional leadership and influencing skills, including the ability to drive alignment and accountability without direct authority.
Strong judgment in balancing delivery rigor, organizational complexity, team autonomy, and the risk of unnecessary process.
Excellent written and verbal communication skills, including communicating progress, risks, tradeoffs, and recommendations to senior leaders and multidisciplinary teams.
Experience using or implementing AI-assisted workflows, automation, or emerging technologies to improve product development, engineering delivery, or organizational processes.
Ability to operate independently in ambiguous environments, identify improvement opportunities, and lead change from diagnosis through implementation and adoption.

Responsibilities

Define, implement, and continuously improve Product and Engineering operating processes from roadmap through planning, design, development, launch, and learning.
Partner with Product and Engineering leadership to translate roadmap priorities into executable plans with appropriate sequencing, ownership, and accountability.
Establish practices for planning, scoping, sequencing, dependency management, delivery reviews, and retrospectives.
Lead program management for high-priority cross-functional initiatives involving Product, Engineering, Design, Data, and business teams.
Provide visibility into commitments, progress, risks, capacity constraints, dependencies, and tradeoffs so teams and leaders can act early.
Diagnose recurring causes of missed commitments, stalled work, inefficient handoffs, and delivery friction, and lead changes addressing underlying causes.
Lead implementation and adoption of an AI-enabled development lifecycle, embedding effective AI uses across discovery, planning, design, development, testing, documentation, and operational work.
Establish and monitor delivery-performance measures, including predictability, cycle time, throughput, quality, and process-improvement impact.
Review Product and Engineering operating practices using delivery evidence and team feedback to determine what to simplify, standardize, improve, or eliminate.
Build and strengthen program-management practices that promote rigor, alignment, and accountability without unnecessary process.