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About This Role
The VP of AI COE Product Management \& Adoption owns adoption and realized value for our AI Marketplace tools (Claude, Gemini, Copilot) and our three strategic custom platforms: Loom, MemX, and Forge49, and the colleague experience that spans them. Building capable platforms is necessary but not sufficient; value is created only when colleagues adopt the governed path and change how they work. This role is accountable for that outcome, and for ensuring the experience across platforms is coherent enough that they will.
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This is a product and adoption function, not a change\-management or training function. Each engineering leader is accountable for whether a platform is built and operated well; this leader is accountable for whether it is adopted, trusted, and delivering measured value, and for carrying the needs of the divisions back into what the engineering pillars build next. Because a colleague’s work moves across Loom, MemX, and Forge49 in a single workflow, no individual platform team owns the end\-to\-end experience. This role owns that experience as a product.
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The role is scoped deep rather than broad; it is accountable for the experience, adoption, and realized value of the three strategic platforms, with clean handoffs at the enterprise boundary, keeping the role focused on the strategic platforms rather than absorbed into run\-the\-business breadth.
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The Team
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The team is organized as a small senior core with a federated adoption reach. The core, cross\-platform product management, experience and design research, marketplace and catalog curation, and adoption measurement, stays lean because its leverage is judgment and orchestration, not volume. The labor\-intensive work of adoption is federated: a central enablement hub orchestrates a champions network embedded in the divisions, rather than staffing a large central training organization. The team is intentionally the leanest of the CoE’s direct\-report functions. Its authority comes from mandate, not headcount.
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The core is organized across four functions, each owning a distinct part of the demand\-to\-value loop: Cross\-Platform Product Management, Experience \& Design Research, AI Marketplace \& Catalog, and Adoption Measurement.
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Scope of Responsibilities
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Cross\-Platform Product Management
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- Own the end\-to\-end colleague experience that spans AI Marketplace tools, Loom, MemX, and Forge49, the product no single engineering pillar owns, defining how a colleague’s work moves across platforms as one coherent journey.
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- Overseeing engineering pillars’ roadmaps. Adoption evidence sets what Platform Engineering teams build next. The pillars own how and how well; this team owns what matters most and for whom. The CDAO arbitrates when demand and feasibility collide.
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- Maintain product\-minded ownership of the three\-platform experience, validated through continuous engagement with colleagues across R\&D, Commercial, PGS, and enabling functions.
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Adoption \& Enablement
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- Own the adoption model for the three platforms, including role\-cohort rollout on a regular cadence, so capability reaches the colleagues who carry the heaviest load first.
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- Operate a central enablement hub that owns the rollout method, AI literacy, and practitioner training, focused on how colleagues think and work with these tools, not only how to click through them.
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- Orchestrate a federated champions network. Champions are embedded in the divisions on a hub\-and\-spoke model, dotted\-line and division\-funded, not centralized headcount. The hub sets the standard and the cadence; the divisions supply the reach.
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- Own the feedback path from the field back into the roadmap, ensuring adoption friction is surfaced as a prioritization signal rather than absorbed silently.
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Experience \& Design Research
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- Own colleague journey mapping and friction analysis across the three platforms, the qualitative discipline that makes “voice of the divisions” a practice rather than a slogan.
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- Investigate why colleagues route around the governed path, and translate those findings into product and roadmap direction for the engineering pillars.
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- Serve as the qualitative complement to adoption measurement: measurement shows what is happening, design research explains why.
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AI Marketplace \& Catalog
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- Own the AI marketplace and catalog scoped to AI\-native reusable assets and the three strategic platforms, the reuse\-before\-build front door that surfaces duplication before a second team builds the same thing.
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- Curate the prompt and skill\-pattern library, the approved patterns and reusable workflows colleagues call instead of a blank prompt box. This is content and practice curation; the registry and runtime infrastructure are owned by the engineering pillars.
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- Coordinate with the Head of Portfolio and Operations on reuse intelligence. Enterprise\-wide reuse across all software demand sits with Portfolio and Operations; this catalog is the AI\-native, three\-platform view that feeds it.
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Adoption Measurement and Value Realization
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- Own adoption measurement for the three platforms as the source of truth, governed\-path versus shadow\-path usage, reuse rate, leakage, and hours recovered. This is the steering signal that drives prioritization, not a downstream report card.
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- Own value realization for AI Marketplace tools, Loom, MemX, and Forge49, and report it up to Portfolio and Operations on their shared value standard. Portfolio and Operations owns the value\-measurement methodology and the enterprise scorecard; this team applies that standard to the three platforms and AI Marketplace tools and reports conformant results. Enterprise aggregation across Tableau, Snowflake, and all other investments sits with Portfolio and Operations.
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- Feed the three\-platform adoption and value metrics into the AI value scorecard as its source for those platforms, so there is one instrumentation pipe and one owner rather than competing dashboards.
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Key Relationships
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- Heads of AI \& Agentic and Data \& BI Platform Engineering, the daily relationship. This role supplies the binding demand signal that shapes their roadmaps; they supply feasibility, cost, and sequencing reality. A productive peer tension, arbitrated by the CDAO.
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- Head of Portfolio and Operations, the enterprise boundary. Demand intake, run\-the\-business demand, and portfolio\-level value aggregation live there; this role hands off at both ends of the loop and reports three\-platform value up on their shared standard.
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- Head of Trusted AI, this role surfaces trust as an adoption barrier when colleagues will not use a system they do not trust, and feeds it to Trusted AI, which owns the responsible\-use standard. This role never owns governance policy, the accelerator and the brake stay separate.
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- Division leaders across R\&D, Commercial, PGS, and enabling functions, the stakeholder relationships through which adoption actually propagates and through which the champions network is federated.
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Leadership Responsibilities
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- Lead, develop, and grow a small senior core across four functions, Cross\-Platform Product Management, Experience \& Design Research, AI Marketplace \& Catalog, and Adoption Measurement, and orchestrate a federated champions network beyond it.
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- Serve as a senior voice in the CoE leadership team, contributing to enterprise AI strategy and operating\-model decisions as the authority on the colleague experience and adoption.
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- Build a team culture that treats adoption as a product outcome earned on merit, not imposed by mandate, the conviction that the governed path wins because it is genuinely the better path.
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- Champion a practitioner community across the federated functions, propagating adoption through embedded champions rather than only through central programs.
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Basic Qualifications
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- Master's degree in Computer Science, Information Systems, Engineering, or a related discipline. Significant relevant experience with a Bachelor's degree may be considered in lieu of a Master's degree.
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- 15\+ years of progressive experience in enterprise product management, digital adoption, or platform go\-to\-market, with a minimum of 10 years in a senior leadership role.
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- Proven track record of driving adoption of enterprise platforms at scale in a large, complex, federated organization, measured by realized usage and value, not launch activity.
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- Demonstrated ability to own a cross\-platform product experience spanning multiple engineering teams that this role does not directly manage, through influence and a credible prioritization mandate.
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- Strong command of adoption and value measurement, defining metrics, instrumenting them, and translating them into an executive narrative connected to enterprise outcomes.
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- Exceptional cross\-functional credibility: able to carry equal authority with engineering leaders, division heads, and senior business stakeholders.
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Preferred Qualifications
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- Experience standing up a champions or practitioner community on a federated, hub\-and\-spoke model.
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- Background in design research or service design, or a track record of embedding it in a product organization.
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- Familiarity with agentic AI and modern AI\-native colleague tools, and the workflow change they require.
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- Experience in a regulated industry where adoption must respect governance and validation boundaries
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Last day to apply: August4, 2026
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Work Location Assignment: Hybrid
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The annual base salary for this position ranges from $274,000\.00 to $456,600\.00\. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 30\.0% of the base salary and eligibility to participate in our share based long term incentive program. We offer comprehensive and generous benefits and programs to help our colleagues lead healthy lives and to support each of life’s moments. Benefits offered include a 401(k) plan with Pfizer Matching Contributions and an additional Pfizer Retirement Savings Contribution, paid vacation, holiday and personal days, paid caregiver/parental and medical leave, and health benefits to include medical, prescription drug, dental and vision coverage. Learn more at Pfizer Candidate Site – U.S. Benefits \| (uscandidates.mypfizerbenefits.com). Pfizer compensation structures and benefit packages are aligned based on the location of hire. The United States salary range provided does not apply to Tampa, FL or any location outside of the United States. This role is posted in multiple locations. If you are applying for the role in an secondary job posting location where pay transparency regulations apply, your Talent Advisor will share the local pay information with you during the first interview.
Relocation assistance may be available based on business needs and/or eligibility.
Candidates must be authorized to be employed in the U.S. by any employer.
U.S. work visa sponsorship (such as TN, O\-1, H\-1B, etc.) is not available for this role now or in the future.
Sunshine Act
Pfizer reports payments and other transfers of value to health care providers as required by federal and state transparency laws and implementing regulations. These laws and regulations require Pfizer to provide government agencies with information such as a health care provider’s name, address and the type of payments or other value received, generally for public disclosure. Subject to further legal review and statutory or regulatory clarification, which Pfizer intends to pursue, reimbursement of recruiting expenses for licensed physicians may constitute a reportable transfer of value under the federal transparency law commonly known as the Sunshine Act. Therefore, if you are a licensed physician who incurs recruiting expenses as a result of interviewing with Pfizer that we pay or reimburse, your name, address and the amount of payments made currently will be reported to the government. If you have questions regarding this matter, please do not hesitate to contact your Talent Acquisition representative.
EEO \& Employment Eligibility
Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, disability or veteran status. Pfizer also complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Pfizer is an E\-Verify employer. This position requires permanent work authorization in the United States.
Pfizer endeavors to make www.pfizer.com/careers accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process and/or interviewing, please email [email protected]. This is to be used solely for accommodation requests with respect to the accessibility of our website, online application process and/or interviewing. Requests for any other reason will not be returned.
To learn more about acceptable and prohibited uses of AI during the recruitment process, please review our candidate AI\-use guidelines available on Pfizer Careers.
Information \& Business Tech
Salary Context
This $274K-$456K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Pfizer, 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 Required
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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($365K) sits 67% above the category median. Disclosed range: $274K to $456K.
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.
Pfizer AI Hiring
Pfizer has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $271K - $500K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 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).
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 3,708 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.
Frequently Asked Questions
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