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Chief of Staff, Enterprise AI
- JR-163585
- Hybrid
- Toronto
- Redwood City
- Dallas
- General Management
- Full time
Who are we?
Equinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet.
A place where bold ideas are welcomed, human connection is valued, and everyone has the opportunity to shape their future. A career at Equinix means being at the center of shaping what comes next and amplifying customer value through innovation and impact. You’ll work across teams, influence key decisions, and help shape the path forward. You’ll find belonging, purpose, and a team that welcomes you—because when you feel valued, you’re empowered to do your best work. Job Summary
Serve as a strategic advisor and execution partner to the Chief AI Officer, helping shape and deliver Equinix’s enterprise AI agenda. The role spans AI strategy and governance, enterprise adoption and value realization, organizational capability, and the external AI ecosystem. Translate priorities into funded plans and measurable outcomes, strengthen executive decision-making, and enable the AI leadership team to operate effectively across a global organization.
The successful candidate combines independent judgment, practical AI fluency, and hands-on execution. They anticipate what needs attention, challenge assumptions constructively, and take ownership of complex work from initial framing through completion.
Responsibilities
Strategic Planning & Executive Advisory
- Partner with the CAIO to define and communicate the enterprise AI strategy, priorities, and investment roadmap, connecting internal transformation with Equinix’s broader AI business and ecosystem strategy
- Develop decision briefs with clear recommendations, alternatives, financial implications, and risks. Provide candid advice and test the assumptions behind major proposals
- Assess emerging AI capabilities, competitors, and partnerships, distinguishing developments that warrant action from those that warrant monitoring
- Lead strategic initiatives and special projects from problem definition through delivery, establishing ownership and transitioning ongoing responsibilities to the appropriate teams
Business Operations & Execution
- Own the AI leadership team’s operating rhythm, including planning, portfolio reviews, leadership meetings, and offsites. Structure forums around decisions, with clear owners, deadlines, and follow-through
- Drive portfolio visibility across priorities, dependencies, investment, and results. Work with Finance and business owners to validate value assumptions and distinguish realized benefits from forecasts
- Coordinate budget and workforce planning with Finance and HR. Surface resource trade-offs and recommend where to accelerate, adjust, or stop investment
- Resolve execution barriers with accountable leaders and escalate material risks early, with options and a recommended course of action
- Represent the CAIO as delegated, exercising judgment within agreed decision rights and keeping the CAIO informed of consequential commitments
Cross-Functional Alignment & Stakeholder Engagement
- Build trusted partnerships with executive leaders, Chiefs of Staff, and business owners to secure sponsorship and drive enterprise AI adoption and value realization
- Resolve cross-functional dependencies and competing priorities. Make ownership explicit and address difficult issues directly and constructively
- Lead preparation of Board and executive materials, decision memos, and organizational communications, translating technical detail into clear business implications and specific asks
- Prepare the CAIO for customer, partner, and industry engagements. Define the purpose of each engagement and drive follow-through, aligning external messaging with Communications and Marketing
Organizational Effectiveness & Transformation
- Partner with the AI leadership team and HR on operating model evolution, workforce priorities, and leadership development. Identify capability gaps and clarify responsibilities as the organization scales
- Coordinate with AI governance, Security, Legal, and other control functions to bring responsible AI requirements and unresolved risk decisions into portfolio planning and executive reviews
- Advance AI fluency and change initiatives with business leaders, using adoption and outcome evidence to identify what needs to change
- Improve how the organization works through simple processes and practical use of AI, reducing manual reporting and unnecessary meetings. Model a culture of High Standard, Low Bureaucracy, Zero Blame
Qualifications
Knowledge & Experience
- 10+ years of experience in strategy, business operations, consulting, Chief of Staff, product, data, AI, or related leadership roles, with demonstrated ownership of execution and business results
- Strong understanding of enterprise AI and technology operating models, including generative AI, agents, shared platforms, adoption barriers, and AI economics and risk. Able to engage credibly with technical and business leaders
- Proven experience delivering complex initiatives across a large, global, matrixed organization, including resolving competing priorities and recovering work that is off track
- Strong financial and analytical acumen, with experience in business cases, investment prioritization, budget planning, and benefits measurement
- Experience personally developing executive communications, Board materials, and strategic plans that require limited revision and support consequential decisions
- Experience partnering with C-suite executives through ambiguity and organizational change. Prior Chief of Staff experience is valuable; equivalent operating leadership experience is equally relevant
Skills & Attributes
- Strategic judgment: Frame the right problem, make trade-offs, and offer an independent point of view
- Execution discipline: Take ownership without waiting for detailed direction and close commitments reliably
- Executive communication: Synthesize complex issues into concise, accurate writing and clear discussion
- Trust and discretion: Handle sensitive matters with integrity and the confidence to challenge respectfully
- Influence and collaboration: Build credibility across functions and hold peers accountable while preserving trust
- Learning agility: Develop technical and business context quickly, use AI tools effectively, and verify their outputs
- Calm leadership: Remain effective under pressure, recognize when to act independently and when to escalate, and put team outcomes first
The targeted pay range for this position in the following location is / locations are:
United States - Redwood City Office GHQ : 213,000 - 319,000 USD / Annual
United States - Dallas Infomart Office DAI : 177,000 - 265,000 USD / Annual
Canada - Toronto Office TRO : 166,000 - 248,000 CAD / Annual
Our pay ranges reflect the minimum and maximum target for new hire pay for the full-time position determined by role, level, and location.The pay range shown is based on our compensation structure in place at the time of posting and may be updated periodically based on business needs. Individual pay is based on additional factors including job-related skills, experience, and relevant education and/or training.
The targeted pay range listed reflects the base pay only and does not include bonus, equity, or benefits. Employees are eligible for bonus, and equity may be offered depending on the position.
Equinix Benefits
As an employee, you become important to Equinix’s success. We ensure all your benefits are in line with our core values: competitive, inclusive, sustainable, connected and efficient. We keep them competitive within the current marketplace to ensure we’re providing you with the best package possible. So, wherever you are in your career and life, you’ll be able to enhance your experience and bring your whole self to work.
Employee Assistance Program: An Employee Assistance program is available to all employees.
US Benefits: - Insurance: You may enroll in health, life, disability and voluntary plans that are designed for you and your eligible family members. - Retirement: You and Equinix may contribute to a retirement plan to help you plan for your financial future. - Paid Time Off (PTO) and Paid Holidays: You will receive an accrued amount of PTO each pay period along with various paid holidays for you to rest and recharge. Eligibility requirements apply to some benefits. Benefits are subject to change and may be subject to specific plan or program terms. Canada Core Benefits: - Insurance: You may enroll in healthcare coverage that is designed to complement the provincial healthcare system, along with life, disability and optional benefit plans that are designed for you and your eligible family members. - Retirement: You may also enroll in Equinix-sponsored retirement or savings plans: Defined Contribution Pension Plan (DCPP), Group Retirement Savings Plan (RRSP) and Tax-Free Savings Plan (TSFA). - Vacation and Paid Holidays: Equinix offers both vacation and personal time, along with various paid holidays for you to rest and recharge. Eligibility requirements apply to some benefits. Benefits are subject to specific plan or program terms, and to change at Equinix discretion. Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability. If you are a qualified candidate and need assistance or an accommodation, please let us know by completing form.
Equinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law.
We use artificial intelligence in our hiring process. Learn more here.
This posting is a new position within our organization.
Salary Context
This $213K-$319K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Equinix, 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 $212,000 based on 11,866 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($266K) sits 25% above the category median. Disclosed range: $213K to $319K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Equinix AI Hiring
Equinix has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Redwood City, CA, Dallas, TX, US. Compensation range: $319K - $319K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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