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About Analog Devices
Analog Devices, Inc. (NASDAQ: ADI ) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers. With revenue of more than $11 billion in FY25, ADI ensures today's innovators stay Ahead of What's Possible. Learn more at www.analog.com and on LinkedIn and X .
Senior Principal AI Technology Strategist
Role Summary
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.
About the Technology Strategy Board (TSB)
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.
Key Responsibilities
1\. AI Technology Strategy (Edge \& Enterprise)
- 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.
2\. Principled “AI for Design” Strategy
- 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.
3\. Strategic Research \& Outside‑In Scanning
- 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.
5\. Cross‑Domain Integration
- 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.
6\. Executive‑Level Communication
- 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.
Reporting \& Organizational Context
- Reports to: David Ryan, Head of Business Discovery
- Role type: Senior individual contributor (P6\)
- Primary interfaces: Paul Golding, VP of Edge and Enterprise AI, Technology Strategy Board members, Analog Garage, senior engineering and software leaders, selected external partners
- Scope: Enterprise‑level influence with deep focus on AI strategy and AI‑enabled software and design workflows
Qualifications
Required
- 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.
- Exceptional ability to synthesize complex technical topics into clear, executive‑level narratives.
Preferred
- 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.
What Success Looks Like
- TSB and Edge \& Enterprise AI leadership are consistently better prepared to make long‑horizon AI investment decisions.
- AI for design strategy is principled, coherent, and aligned with long‑term differentiation rather than tool‑chasing.
- Strategy artifacts are clear, decision‑ready, and respected for their rigor and insight.
- ADI differentiation and identity in the age of AI abundance are well articulated using a range of long\-horizon frameworks, including emergent ones pertaining to AI scaling laws (e.g. METR, etc)
*For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export licensing approval from the U.S. Department of Commerce \- Bureau of Industry and Security and/or the U.S. Department of State \- Directorate of Defense Trade Controls. As such, applicants for this position – except US Citizens, US Permanent Residents, and protected individuals as defined by 8 U.S.C. 1324b(a)(3\) – may have to go through an export licensing review process.*
*Analog Devices is an equal opportunity employer. We foster a culture where everyone has an opportunity to succeed regardless of their race, color, religion, age, ancestry, national origin, social or ethnic origin, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, parental status, disability, medical condition, genetic information, military or veteran status, union membership, and political affiliation, or any other legally protected group.*
*EEO is the Law:* *Notice of Applicant Rights Under the Law* *.*
Job Req Type: Experienced
Required Travel: Yes, 10% of the time
Shift Type: 1st Shift/Days
The expected wage range for a new hire into this position is $260,360 to $357,995\.
- Actual wage offered may vary depending on work location , experience, education, training, external market data, internal pay equity, or other bona fide factors.
- This position qualifies for a discretionary performance\-based bonus which is based on personal and company factors.
- This position includes medical, vision and dental coverage, 401k, paid vacation, holidays, and sick time , and other benefits.
Salary Context
This $260K-$357K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Analog Devices, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($309K) sits 73% above the category median. Disclosed range: $260K to $357K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Analog Devices AI Hiring
Analog Devices has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Jose, CA, US. Compensation range: $357K - $357K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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