Senior Engineering Program Manager, Lead - Service Special Projects

Apple Apple · Big Tech · Cupertino, CA +1 · Software and Services

Senior Engineering Program Manager to lead ambitious, multi-year, cross-functional AI/ML programs from inception. This hands-on role requires technical depth to manage client software, backend services, data systems, applied ML, evaluation, tooling, and platform engineering. Responsibilities include owning the plan of record, identifying risks, influencing teams, and establishing operating rhythms. Requires experience leading AI-first programs end-to-end, with a strong understanding of the AI stack and evaluation-driven development.

What you'd actually do

  1. Building and owning a credible plan of record — roadmap, milestones, dependencies, critical path — for programs with dozens of engineers and multiple partner teams.
  2. Landing AI/ML capabilities in production — moving a system from research prototype through evaluation, hardening, and launch, with explicit quality bars and rollback criteria.
  3. Identifying risks early, developing mitigation strategies, and driving conflict resolution across senior technical stakeholders.
  4. Navigating ambiguity — creating clarity when scope, requirements, and priorities are still forming.
  5. Tracking many parallel work-streams in real time without losing the thread on any of them.

Skills

Required

  • 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.

Nice to have

  • 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.

What the JD emphasized

  • AI-first programs end to end
  • evaluation-driven engineering programs
  • working literacy with the modern AI stack

Other signals

  • AI/ML capabilities in production
  • AI-first programs end to end
  • working literacy with the modern AI stack
  • evaluation-driven engineering programs