AI Technology Strategist

Analog Devices•San Jose, CA

2d•Hybrid

About The Position

The AI Technology Strategist will be a senior individual contributor aligned with the Edge and Enterprise AI organization and directly supporting ADI’s Technology Strategy Board (TSB). The role is responsible for developing principled, evidence-based AI technology strategies across Edge AI, Enterprise AI, and AI for design, including software and developer-facing workflows. This role exists to do the deep strategic legwork: scanning emerging AI technologies, synthesizing internal and external signals, and translating them into clear strategy options, roadmaps, and narratives that inform executive decision-making. The role is not a product execution or people-management position. Instead, it focuses on long-horizon thinking, cross-domain integration, and strategic clarity at the intersection of AI, software, and physical systems. The Technology Strategy Board (TSB) is a senior, cross-disciplinary body chartered to help shape the company’s long-term technology direction. Comprised of Fellows and senior technology leaders from across the organization, the TSB advises the CEO and Executive Leadership Team on emerging technologies, strategic risks, and future investment priorities. The TSB operates through focused workstreams (e.g., Edge AI, AI for R&D, heterogeneous compute, physical technology) and produces strategy vision documents, multi-year technology roadmaps, and executive readouts. Its mandate is to help the company make fewer, bigger bets, avoid strategic blind spots, and ensure coherence across technology domains. The Senior Principal AI Technology Strategist does not sit on the TSB, but works closely with TSB members to support their work through structured research, synthesis, and strategic framing.

Requirements

12+ years of experience spanning AI, advanced computing, software platforms, or technology strategy.
Demonstrated experience developing technology strategy, not just executing roadmaps or products.
Strong understanding of AI systems across models, software stacks, tooling, and compute constraints—particularly at the edge.

Nice To Haves

Experience supporting CTO offices, strategy boards, or senior technical councils.
Background that spans software and hardware, or AI and physical systems.
Familiarity with AI-enabled engineering, design automation, or developer productivity tools.
Credibility with senior technical leaders without relying on formal authority.

Responsibilities

Develop coherent AI technology strategies spanning edge-constrained systems, enterprise platforms, and hybrid deployments.
Identify where AI can create durable platform-level differentiation versus incremental feature value.
Translate AI trends into implications for compute architectures, software stacks, system design, and long-term capability building.
Lead strategic analysis of AI for design and R&D, including software-centric workflows such as code generation, verification, simulation, data curation, and engineering productivity tools.
Develop principled positions on where AI meaningfully accelerates design versus where human-driven or physics-anchored approaches remain essential.
Frame AI for design not as isolated tools, but as part of an integrated R&D strategy that reinforces long-term competitive advantage using deep computation and related AI methods.
Continuously scan academic research, startups, platform vendors, and industry ecosystems relevant to AI, software, and design automation.
Identify weak signals and emerging inflection points before they become obvious market narratives.
Benchmark internal capabilities against external trajectories to highlight strategic gaps, risks, and opportunities.
Support TSB workstreams through structured research briefs, strategy memos, and synthesis documents.
Prepare decision-ready materials for TSB and Executive Leadership Team readouts, framing clear options, trade-offs, and implications.
Help ensure consistency and coherence across TSB strategy outputs, particularly where AI intersects multiple domains.
Bridge AI strategy across hardware, software, tools, and physical systems, avoiding siloed or software-only perspectives.
Work with domain experts across engineering, software, and research organizations to validate assumptions while maintaining an enterprise-level view.
Connect near-term AI tool adoption with long-term platform and capability strategy.
Produce high-quality written strategy artifacts, including vision documents, technology roadmaps, and executive summaries.
Clearly communicate uncertainty, optionality, and risk—supporting judgment rather than false precision.
Help shape the company’s external and internal narrative around AI: what we lead in, and what we intentionally choose not to pursue.