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ROLE SUMMARY
The Senior Director, Applied AI is a system\-level AI and architecture leader who shapes enterprise and domain\-level direction by connecting technological possibility to business value, operating\-model design, and measurable outcomes. You determine what should be built and why it matters, how it should be governed, and how to keep solutions coherent at scale — working through strategic influence, architectural direction, standards, and decision frameworks.
You are also a builder. You stay close enough to the technology to keep your judgment credible — architecting and reviewing the hardest technical decisions, and setting the engineering bar others meet. And you lead a mixed team of full\-time engineers, contractors, and vendor partners whose work you scope, direct, and hold to Pfizer's quality and security standards.
As part of Pfizer's commercial AI organization, you help deliver on Pfizer's ambition to be the most AI\-forward company — ensuring AI investments translate into coherent, enterprise\-wide value rather than disconnected local solutions.
Why This Role Matters
Pfizer's purpose — breakthroughs that change patients' lives — has always depended on the company's willingness to transform how it works, not only what it discovers. Being the most AI\-forward company, and reinventing how we work from end to end, is the next chapter in that story. Realizing that ambition depends on doing AI coherently and at scale, not as a set of disconnected efforts. This role ensures the organization builds AI that is sound, reusable, and aligned — preventing fragmentation and technical debt while staying technical enough to know whether what is proposed is actually buildable. You are responsible for ensuring AI investments produce real outcomes, reusable capabilities, and a unified direction.
Candidate Profile
You are a system thinker and transformation shaper, comfortable under ambiguity and at enterprise scale, who has kept your hands on the technology. You combine strong technical judgment and current hands\-on ability with business acumen, framing AI not as a technology initiative but as a driver of operational and strategic transformation. You are equally effective influencing senior executives, guiding technical leaders, and directing the engineers, contractors, and vendors who do the build.
ROLE RESPONSIBILITIES
1\) Enterprise Strategy \& Transformation — Shape and evolve enterprise or domain\-level AI strategy, grounded in clear business value and transformation outcomes. Connect AI initiatives explicitly to measurable impact, and influence leadership on where AI should be applied and how it should reshape processes and operating models.
2\) System\-Level Architecture \& Operating\-Model Design — Design and evolve enterprise AI operating models, the target technology landscape, and the relationships between platforms, capabilities, and embedded solutions. Create decision frameworks and architectural principles that drive consistent decisions across teams.
3\) Portfolio \& Trade\-off Leadership — Drive portfolio\-level architectural decisions across initiatives, platforms, and reusable capabilities. Balance value vs. cost, speed vs. governance, standardization vs. flexibility, and innovation vs. reliability, steering toward enterprise reuse and away from duplication.
4\) Standards, Governance \& Enterprise Coherence — Define and steward “what good looks like” across AI architecture, solution design, and operational execution — quality, evaluation and governance, integration patterns, reuse and modularity. Actively prevent fragmentation, architectural drift, and systemic technical debt.
5\) Hands\-on Technical Leadership — Stay hands\-on where it matters — architecture and code review with modern AI and agent frameworks — to validate emerging technology, de\-risk decisions, and keep direction grounded in what is buildable. You lead from the front on the hardest problems rather than directing from a distance.
6\) Team, Vendor \& Delivery Leadership — Lead a mixed team of full\-time engineers, contractors, and vendor partners across one or more initiatives. Scope and direct vendor work, set quality and security standards, manage performance and spend, and ensure critical capability and IP stay with Pfizer. Mentor Directors and engineers and raise the technical bar across the group.
7\) External Signal Integration \& Thought Leadership — Continuously assess emerging technologies, the vendor landscape, industry trends, and research. Translate signal into practical, enterprise\-relevant implications, distinguish hype from viable solutions, and act as an internal and external thought leader.
Technical Profile
The Senior Director is defined not by being the deepest specialist in every area, but by the ability to synthesize across them — with credible, current hands\-on ability in a subset. Expected breadth across: agentic systems and LLMs; traditional AI/ML and neural networks; data and business analytics and data architecture; AI\-readiness of data and retrieval architecture; solution\-design architecture; tracing, monitoring and observability; and AI guardrails and governance. The differentiator is cross\-domain synthesis and orchestration, paired with enough hands\-on depth to lead the build.
QUALIFICATIONS
Basic Qualifications
- 12\+ years in AI, data, or software architecture, with significant experience at enterprise or multi\-domain scale.
- Proven ability to shape enterprise or domain\-level strategy and architecture direction, tied to measurable business outcomes.
- Current hands\-on technical proficiency — able to read, write, build, and review code with modern AI / agent frameworks.
- Experience leading full\-time engineers, contractors, and vendor partners to deliver production\-grade systems.
- Strong experience in operating\-model design, governance frameworks, and architectural standardization.
- Demonstrated ability to influence senior executive stakeholders.
- Deep understanding across AI/ML and generative AI, system architecture, data architecture, and software engineering.
- Excellent communication, translating complexity into clear business outcomes.
Preferred Qualifications
- Experience in large, regulated enterprises (e.g., pharma, healthcare, finance).
- Familiarity with AI governance and guardrails, AI\-readiness and retrieval architectures, and observability and monitoring.
- Experience shaping enterprise AI strategies, roadmaps, or transformation programs.
- Experience managing vendor / contractor budgets and statements of work.
- Recognized internal or external thought\-leadership presence.
Last Day to Apply: June 14, 2026
Location: Selected colleague must be onsite 2\.5x weekly.
The annual base salary for this position ranges from $214,900\.00 to $358,100\.00\. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 22\.5% 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.
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 $214K-$358K 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 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 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. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($286K) sits 60% above the category median. Disclosed range: $214K to $358K.
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.
Pfizer AI Hiring
Pfizer has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, Data Engineer. Based in New York, NY, US. Compensation range: $165K - $358K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national 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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