Are you a seasoned program leader who thrives at the frontier of artificial intelligence, agentic systems, and large-scale hardware innovation? We’re looking for a Senior Technical Program Manager to own and drive the AI transformation strategy across our entire Hardware Engineering organization. In this autonomous role, you will set the multi-year roadmap for how AI agents, orchestration frameworks, and intelligent systems reshape how our hardware teams design, build, validate, and ship world-class products. You will operate as a trusted thought partner to VP- and SVP-level leaders, define the strategic AI portfolio across hundreds of engineers, and personally architect the operating model that turns AI from experimentation into durable, org-wide capability. You will be expected to identify second- and third-order opportunities others miss, navigate ambiguity without a playbook, and deliver outcomes that materially change the trajectory of the business.

● Set the AI Transformation Strategy — Define and own the multi-year AI roadmap for Hardware Engineering, aligning org-wide investments with business priorities and influencing executive decisions on capability bets, build-vs-buy, and platform direction.

● Partner on Architecture of Agentic Systems for Engineering Workflows — Lead the design and deployment of AI agents, multi-agent orchestration patterns, and RAG-based systems that automate and augment hardware design, validation, supply chain, and program management workflows.

● Drive Org-Wide Adoption & Change — Own the adoption strategy for AI capabilities across thousands of engineers; build the coalitions, incentives, and operating mechanisms that move AI from pilot to default practice.

● Govern a Strategic Portfolio — Establish the governance, prioritization, and resource-allocation frameworks for the full AI portfolio; chair cross-functional steering reviews and make trade-off calls that span multiple VPs.

● Partner Deeply on Technical Direction — Work shoulder-to-shoulder with SDEs and platform teams to shape model selection, evaluation frameworks, MLOps practices, and orchestration architecture; provide credible technical pushback and direction.

● Influence Without Authority at the Highest Levels — Communicate strategy, risks, and outcomes through executive narratives, OP1/OP2 documents, and quarterly business reviews; influence VP-level stakeholders and represent the org externally where appropriate.

● Build Repeatable AI Operating Models — Codify successful patterns into reusable playbooks, reference architectures, and adoption frameworks that scale beyond your immediate org.

● Define Outcome Metrics That Matter — Establish the success criteria, north-star metrics, and ROI frameworks for AI investment; tie program outcomes directly to engineering velocity, product quality, and unit economics.