Job description

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 workstreams, you will shape the program-management practice — mentoring more junior EPMs, standardizing how progress is tracked, and raising the bar for how we plan and communicate. 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. 5+ years 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. BS in Computer Science, Engineering, or a related technical field, or equivalent experience. Advanced degree preferred. Building and owning a credible plan of record — roadmap, milestones, dependencies, critical path — for programs with dozens of engineers and multiple partner teams. Landing AI/ML capabilities in production — moving a system from research prototype through evaluation, hardening, and launch, with explicit quality bars and rollback criteria. Identifying risks early, developing mitigation strategies, and driving conflict resolution across senior technical stakeholders. Navigating ambiguity — creating clarity when scope, requirements, and priorities are still forming. Tracking many parallel workstreams in real time without losing the thread on any of them. Influencing and aligning teams without direct authority — including senior engineers, architects, applied researchers, and cross-functional leaders. Establishing durable operating rhythms — planning cycles, review cadences, escalation paths, post-mortems — that scale with the program. Presenting technical status, trade-offs, and recommendations to executive audiences with precision and honesty.

Requirements

Must-haves

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.
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.
Exceptional written and verbal communication skills, including a track record of writing crisp executive updates and delivering technical narratives to senior leadership.
Building and owning a credible plan of record — roadmap, milestones, dependencies, critical path — for programs with dozens of engineers and multiple partner teams.
Landing AI/ML capabilities in production — moving a system from research prototype through evaluation, hardening, and launch, with explicit quality bars and rollback criteria.
Identifying risks early, developing mitigation strategies, and driving conflict resolution across senior technical stakeholders.
Navigating ambiguity — creating clarity when scope, requirements, and priorities are still forming.
Tracking many parallel workstreams in real time without losing the thread on any of them.
Influencing and aligning teams without direct authority — including senior engineers, architects, applied researchers, and cross-functional leaders.
Establishing durable operating rhythms — planning cycles, review cadences, escalation paths, post-mortems — that scale with the program.
Presenting technical status, trade-offs, and recommendations to executive audiences with precision and honesty.

Nice-to-haves

BS in Computer Science, Engineering, or a related technical field, or equivalent experience. Advanced degree preferred.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaiml

Platform: apple
Effort: medium