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About This Role
### Senior Principal Product Manager \- Gen AI Platforms
- JR\-162297
- Hybrid
- Toronto
- Redwood City
- Dallas
Show More
- Technology Enablement
- 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
Designs, develops and manages the lifecycle of a product or group of products from concept to launch to end of life. Translates market opportunities and customer demand into viable products and services that differentiate Equinix in the market. Sets the vision and strategy for their product ensuring it is competitively positioned and customer\-centric. Manages the product roadmap including features, upgrades and maintenance of the product or product line. Works cross functionally with user experience, engineering, operations, solution architects, marketing and others to design, build and launch new products and/or product features.
Responsibilities
Generative AI, Cloud, and Data Architecture
- Bring strong working knowledge of generative AI, cloud infrastructure, and enterprise data architecture to product and platform decisions
- Partner with engineering and architecture teams to evaluate large language model architectures, AI agents, cloud deployment approaches, and enterprise data pipelines
- Participate meaningfully in technical and architecture reviews, as well as product and roadmap discussions
- Translate technical choices, constraints, and risks into clear business implications that leaders can understand and act on
- Help ensure that AI products are designed for enterprise scale, security, reliability, and reuse
Build, Buy, and Partner Decisions
- Establish a consistent approach for determining when Equinix should build AI capabilities internally, purchase commercial technology, or partner with external providers
- Evaluate cloud AI services, commercial model providers, open source technologies, and enterprise AI platforms
- Assess options based on business value, implementation time, cost, technical fit, security, operational complexity, and long term strategic importance
- Develop clear recommendations supported by financial analysis, technical assessment, and risk considerations
- Present recommendations to senior leaders and support informed investment decisions
Model Strategy and Deployment
- Guide decisions on prompt design, retrieval augmented generation, model customization, fine tuning, and model selection
- Help teams determine when a smaller model may provide better performance, cost, speed, or control than a larger model
- Partner with AI and machine learning engineering teams on deployment approaches across cloud, private infrastructure, and environments with strict performance requirements
- Evaluate emerging approaches such as AI agent coordination, model routing, and hybrid model deployment
- Use model performance data, evaluation results, user feedback, and business outcomes to guide product priorities
Platform Reliability and Responsible AI
- Define product requirements for AI system reliability, availability, performance, monitoring, usage limits, and incident response
- Partner with engineering teams to improve visibility into AI system behavior, model performance, cost, and production issues
- Establish requirements that support traceability, explainability, auditability, fairness, privacy, and regulatory compliance
- Work with Legal, Security, Privacy, and AI Governance teams to incorporate company policies and responsible AI requirements into products and platforms
- Partner with Design to create clear user experiences that explain how AI is being used and provide appropriate user review, control, and approval
Product Decisions and Risk Management
- Lead decisions involving tradeoffs among business value, delivery speed, technical complexity, cost, performance, and risk
- Establish clear and repeatable methods for evaluating major AI product and architecture decisions
- Help teams identify risks early and determine the appropriate level of governance and human oversight
- Balance the need to deliver value quickly with the requirements of security, reliability, and responsible AI
Executive and Cross Functional Leadership
- Build alignment across Product, Engineering, Architecture, Legal, Information Security, Data, Design, and business functions
- Influence decisions through expertise, clear reasoning, and strong working relationships rather than direct authority
- Explain complex technical topics in clear language that is relevant to both technical and business audiences
- Work with senior leaders to connect AI investments to business priorities, customer value, productivity, and operational outcomes
- Develop trusted relationships so that teams engage early when evaluating important AI opportunities or decisions
Industry and Technology Assessment
- Stay current on developments in generative AI, including models, platforms, tools, enterprise applications, and deployment methods
- Evaluate which technologies are relevant to Equinix and which are unlikely to provide meaningful business value
- Develop informed perspectives that help guide product strategy, architecture, partnerships, and investment
- Represent Equinix’s perspective on generative AI internally and, where appropriate, with customers, partners, and the broader industry
Qualifications
- 10 or more years of experience in Product Management, Engineering, Data Science, or a related field
- Significant experience with generative AI, cloud platforms, enterprise data architecture, or AI powered products
- Demonstrated ability to operate as a senior individual contributor across both technical and business topics
- Experience working directly with large language models, prompt design, retrieval augmented generation, model customization, or fine tuning
- Experience evaluating model choices and understanding the tradeoffs among quality, speed, cost, security, and operational complexity
- Experience leading build, buy, and partner decisions for AI, machine learning, data, or enterprise technology capabilities
- Strong understanding of machine learning pipelines, model deployment, model serving, evaluation, and feedback processes
- Working knowledge of distributed systems, cloud architecture, APIs, and enterprise platforms
- Experience designing shared platform capabilities that can support multiple products, teams, or business functions
- Ability to influence senior leaders across technical and business organizations
- Strong written and verbal communication skills, with the ability to explain complex topics clearly to different audiences
- Sound judgment when evaluating new technologies and determining their practical value
- Experience working across functions and geographies in a large organization
- Ability to make progress in areas where requirements, technology, or business needs are still evolving
Preferred Qualifications
- Master’s degree or doctorate in Computer Science, Data Science, Statistics, Engineering, Physics, or a related field
- Experience working with models and platforms from OpenAI, Anthropic, Google, or the open source community
- Experience with machine learning operations, model evaluation, AI monitoring, or model observability tools
- Experience defining service requirements for AI systems, including availability, performance, monitoring, usage limits, and operational support
- Knowledge of responsible AI practices, including explainability, model documentation, evaluation, audit processes, governance, and policy implementation
- Experience working with research, engineering, or innovation teams to bring AI capabilities into production
- Experience designing AI user experiences that support transparency, consent, user review, and appropriate human control
- Experience in data centers, cloud infrastructure, telecommunications, or enterprise technology
- Experience building AI assistants, agents, conversational products, or AI enabled workflows
- Experience with business software, enterprise platforms, or products designed for developers
The targeted pay range for this position in the following location is / locations are:
United States \- Dallas Infomart Office DAI : 177,000 \- 265,000 USD / Annual
United States \- Redwood City Office GHQ : 213,000 \- 319,000 USD / Annual
Canada \- Toronto Office TRO : 182,000 \- 272,000 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 $177K-$319K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $188K across 140 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Equinix, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($248K) sits 15% above the category median. Disclosed range: $177K to $319K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Equinix AI Hiring
Equinix has 4 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span Dallas, TX, US, Chicago, IL, US. Compensation range: $204K - $403K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
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