Senior Engineering Program Manager, Lead - Service Special Projects
Apple · Cupertino, California, United States
Computers and Electronics Manufacturing · 10,001+ employees
About the role
You will lead large-scale, cross-functional engineering initiatives by managing roadmaps, dependencies, and executive-level narratives. You will also act as the connective tissue between engineering, research, and business functions while mentoring other program managers.
What they look for
Requirements
Candidates must have at least 15 years of experience in technical program management, including 3+ years in a lead or principal capacity. Strong technical depth in AI-first programs, machine learning lifecycles, and cross-functional engineering disciplines is required.
Full description
We are looking for a highly seasoned Senior Engineering Program Manager to help get one of our most ambitious multi-year engineering programs off the ground.
This is a hands-on, lead-level role for a seasoned program manager who has experience delivering large-scale and technically deep multi-team efforts from their inception. If you thrive when the problem is broad, the org is scaling up around them, and the technical stakes are high, this may be the role for you!
Description
In this role, you will be the connective tissue across engineering, research, design, and business functions from day one, turning ambitious goals into a coordinated, measurable plans that will drive our team forward.
You will own the end-to-end program management of a large-scale, cross-functional engineering initiative spanning client software, backend services, large-scale data systems, applied machine learning, evaluation, tooling, and platform engineering. You will be accountable for the plan of record — roadmap, milestones, dependencies, risks, and the executive-level narrative — and for the operating cadence that keeps every team aligned on what is expected, by when, and why.
You will need to be technical enough to hold your own in an architectural review and in a candid scoping conversation with a Principal Engineer. You will not write production code, but you will read designs, ask sharp questions, and translate deeply technical discussions into crisp, faithful summaries for leadership. When the program drifts, you will identify it early, name it clearly, and drive the correction with calm authority.
This is a lead role. Alongside your own work-streams, you will shape the program management practice — mentoring other EPMs, standardizing how progress is tracked, and raising the bar for how we plan and communicate.
Minimum Qualifications
Bachelors Degree plus at least 15 years of experience developing program plans and managing complex, cross-functional engineering programs in a technical product environment, with at least 3+ years in a lead or principal-level capacity. Experience leading AI-first programs end to end — programs where the core product value is delivered by machine-learning or large-language-model systems, and where planning, scoping, and quality bars are inseparable from model behavior and evaluation. Demonstrated experience running programs that span multiple engineering disciplines — client, services, data, applied ML, platform, and tooling — end to end. Strong technical depth: able to read engineering design documents, follow architectural discussions across several disciplines, and independently reason about trade-offs, dependencies, and risk. Working literacy with the modern AI stack — model training and evaluation lifecycles, offline vs. online metrics, data pipelines feeding models, latency/cost trade-offs, and the difference between a research result and a production-ready capability. Exceptional written and verbal communication skills, including a track record of writing crisp executive updates and delivering technical narratives to senior leadership.
Preferred Qualifications
MS or other advanced degree. Project Management Certifications. Experience with evaluation-driven engineering programs — where the definition of "done" depends on measured quality against explicit metrics. Fluency with modern engineering tooling — issue trackers, planning systems, dashboards — with a bias toward instrumenting programs so status is derived, not narrated.