Manager, Product Operations

Meta•Sunnyvale, CA

19d•$199,000 - $272,000

About The Position

We’re seeking a Manager, Wrist Product & Technical Operations to lead Hardware NPI execution for Wrist products within Product & Tech Ops. This leader will directly manage Manufacturing Technical Managers (MTMs) and drive end-to-end readiness from early NPI through ramp. This role leads through both direct management and matrixed influence across GSM, DFX, and MTE partners, and regularly aligns with Engineering and Product Management leadership as well as FATP supplier leadership. AI-first efficiency expectation: A core expectation of this leader is to make AI the primary path to driving step-change efficiency in Wearables NPI planning and execution—reducing manual coordination and reporting, increasing decision velocity, improving predictability, and enabling the organization to scale program load without proportional headcount growth.

Requirements

Bachelor degree in Engineering (ME, EE, IE)
Experience driving manufacturing readiness and performance outcomes in partnership with factory and supplier teams
10+ years experience leading complex hardware programs (NPI, manufacturing/technical operations, program leadership, or equivalent)
6+ years people management experience
Experience with product launches in the consumer space
Proven track record on driving program KPIs and ownership on Quality, Cost and Delivery
Experience leading programs in APAC

Nice To Haves

Experience leading executive-level vendor relationships and driving outcomes through complex supplier ecosystems
Experience driving operational efficiency improvements via automation and/or AI with measurable outcomes
Cost leadership experience including CAPEX governance, cost-down programs, and financial tradeoff decision-making
Experience leading manufacturing operations teams

Responsibilities

Own Hardware NPI execution (portfolio operating system): Own end-to-end NPI execution across development phases, including integrated schedules, critical path management, and phase-exit readiness. Build and run the Wearables NPI operating cadence: milestone reviews, cross-functional business reviews, risk and change control, and launch/ramp governance. Create durable program mechanisms (single source of truth, readiness gates, decision logs, escalation paths) so teams can execute with high autonomy and accountability
Manufacturing Readiness Ownership: Manage Manufacturing Technical Managers (MTMs) as the manufacturing owners for Wearables NPI, accountable for: Factory line readiness as a single source of truth enabling NPI builds and execution. 1) Manufacturing process intent/flow and manufacturing plan maturity through NPI 2) Line layout readiness, equipment/fixture planning, and CAPEX planning/spend governance through NPI 3) Ownership of manufacturing outcomes (e.g., FPY/LPY, UPH, efficiency) and clear ramp trajectories 4) Major Issues List rigor and driving failure analysis + corrective actions (FACA) to meet readiness and performance targets 5) Build a high-performing MTM org through hiring, onboarding, coaching, performance management, and clear role expectations
Matrixed leadership across GSM, DFX, MTE & CMM Drive alignment and execution across a matrixed ecosystem of partners including GSM, DFX, and MTE to ensure decisions, requirements, and readiness actions land on time. Ensure clean interlocks between manufacturing readiness, test strategy, DFX requirements, supply/material readiness, and build execution. Establish and enforce cross-functional SLAs for updates, escalations, and deliverables needed to hit build and ramp milestones
Partner with Engineering/Product leadership + FATP supplier executives: Own the governance model for Wearables NPI readiness, aligning Engineering and Product Management leadership and FATP supplier executives on plan-of-record, tradeoffs, and phase-exit criteria. Drive fast, well-documented decisions (schedule ↔ quality ↔ cost), with clear options, recommendations, and accountability. Represent Wearables Product & Tech Ops in executive forums; surface risks early and mobilize cross-org resources to resolve them
Internal database leverage: Make internal systems the single source of truth for: build plans, readiness status, decisions, dependencies, manufacturing metrics/results, and cost tracking. Raise bar on data hygiene: consistent taxonomy, ownership, update SLAs, and adoption across teams. Replace manual reporting with automated rollups; ensure reviews are run directly from canonical data
AI-first execution efficiency: Make AI the default approach to reducing operational friction and improving planning/execution quality—identifying repeatable workflows and replacing manual work with AI-enabled automation. Deploy AI/automation across core NPI workflows (e.g., plan/status synthesis, meeting-to-actions, risk signal extraction, dependency mapping, change-log summarization) with clear accuracy standards and traceability to source data. Set and deliver measurable targets tied to planning/execution efficiency (e.g., reduced manual reporting effort, faster decision cycles, improved milestone predictability, reduced issue aging), and drive adoption through training, workflow integration, and usage/impact tracking. Establish guardrails (quality checks, human-in-the-loop controls, privacy/compliance considerations) to ensure trustworthy AI outputs
Ramp curves + Cost Ownership: Define and drive ramp curves (build volume, yield maturation, UPH/efficiency trajectory), aligning constraints across factory readiness, staffing, and materials. Own cost leadership mechanisms in partnership with Finance/Sourcing, Product Cost Engineering and supplier leadership: 1) CAPEX governance and investment tradeoffs 2) Cost-down roadmaps and variance management 3) Clear visibility into the cost-to-deliver implications of program choices
People leadership: Set clear goals and expectations; coach and develop a team that consistently executes in ambiguity and high stakes environments. Build an environment of operational rigor, transparency, continuous improvement, ownership and accountability and AI-first operational model