Senior Engineering Program Manager, Lead , Service Special Projects

Apple•Cupertino, CA

11d

About The Position

This is a hands-on, lead-level role supporting the launch of an ambitious, multi-year engineering program. The Senior Engineering Program Manager will connect engineering, research, design, and business functions, turning an evolving vision into a coordinated, measurable plan and driving that plan through delivery. The role owns end-to-end program management for a large-scale, cross-functional initiative spanning client software, backend services, large-scale data systems, applied machine learning, evaluation, tooling, and platform engineering. The manager is accountable for the plan of record—including roadmap, milestones, dependencies, risks, and executive narrative—and for maintaining the operating cadence that keeps teams aligned. The role requires enough technical depth to participate effectively in architectural reviews and scoping discussions, interpret engineering designs, ask informed questions, and communicate technical discussions accurately to leadership. It does not involve writing production code. When the program deviates from plan, the manager is expected to identify and communicate the issue early and drive corrective action. In addition to managing their own workstreams, this lead role shapes program-management practices, mentors junior EPMs, standardizes progress tracking, and improves planning and communication.

Requirements

10 or more years of experience developing program plans and managing complex, cross-functional engineering programs in a technical product environment.
At least 3 years in a lead- or principal-level capacity.
At least 5 years leading AI-first programs end to end, where core product value is delivered by machine-learning or large-language-model systems and planning, scoping, and quality bars depend on model behavior and evaluation.
Demonstrated experience managing programs end to end across multiple engineering disciplines, including client, services, data, applied machine learning, platform, and tooling.
Strong technical depth, including the ability to read engineering design documents, follow architectural discussions across disciplines, and reason independently about trade-offs, dependencies, and risks.
Working literacy with the modern AI stack, including model training and evaluation lifecycles, offline and online metrics, model data pipelines, latency and cost trade-offs, and the distinction between research results and production-ready capabilities.
Exceptional written and verbal communication skills, including experience writing concise executive updates and delivering technical narratives to senior leadership.
Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent experience.

Nice To Haves

Advanced degree.
Experience building and owning a credible plan of record—including roadmap, milestones, dependencies, and critical path—for programs involving dozens of engineers and multiple partner teams.
Experience landing AI/ML capabilities in production, from research prototype through evaluation, hardening, and launch, with explicit quality bars and rollback criteria.
Experience identifying risks early, developing mitigation strategies, and resolving conflicts across senior technical stakeholders.
Ability to navigate ambiguity and create clarity while scope, requirements, and priorities are still forming.
Ability to track many parallel workstreams in real time.
Experience influencing and aligning teams without direct authority, including senior engineers, architects, applied researchers, and cross-functional leaders.
Experience establishing durable operating rhythms that scale with a program.
Experience presenting technical status, trade-offs, and recommendations to executive audiences with precision and honesty.

Responsibilities

Own end-to-end program management for a large-scale, cross-functional engineering initiative.
Create and maintain the plan of record, including the roadmap, milestones, dependencies, risks, and executive-level narrative.
Establish and run an operating cadence that keeps teams aligned on deliverables, timing, and rationale.
Coordinate work across client software, backend services, large-scale data systems, applied machine learning, evaluation, tooling, and platform engineering.
Read engineering designs, participate in architectural and scoping discussions, and translate technical topics into clear, faithful leadership updates.
Identify program drift and risks early, communicate them clearly, and drive corrective action and mitigation.
Mentor junior engineering program managers, standardize progress tracking, and raise the quality of planning and communication.
Drive programs from research or prototype through evaluation, hardening, and production launch with explicit quality bars and rollback criteria.
Establish scalable operating rhythms, including planning cycles, review cadences, escalation paths, and post-mortems.
Present technical status, trade-offs, and recommendations to senior leadership.