Interested in this AI/ML Engineer role at Pfizer?
Apply Now →About This Role
ROLE SUMMARY
The Director, Applied AI (Horizon Scanning) is responsible for identifying, evaluating, and translating emerging AI technologies, capabilities, vendors, and industry trends into actionable opportunities for Pfizer’s U.S. Commercial organization. The role serves as the connection point between external AI innovation and U.S. Commercial strategy, ensuring that evolving technologies are assessed through the lens of commercial priorities, customer engagement, marketing, market access, patient solutions, commercial operations, and future business transformation.
Reporting to the Senior Director, Strategy, this leader is responsible for horizon scanning, external technology intelligence, capability assessment, technology scouting, rapid evaluation, and future-state capability planning. The role helps ensure U.S. Commercial remains at the forefront of AI innovation while maintaining focus on business value, technical feasibility, enterprise readiness, and responsible adoption.
The Director partners across AI Partnerships & Ecosystem, Product Management, AI Applications, Data Strategy, Transformation & Delivery, Governance, and Digital to convert external signals into a prioritized pipeline of opportunities, experiments, and roadmap recommendations. When an opportunity requires external engagement, the Director provides the technical and strategic assessment and transitions the opportunity to the AI Partnerships & Ecosystem Lead for partner engagement, coordination, and ongoing relationship management.
WHY THIS ROLE MATTERS
AI is evolving faster than traditional business and technology planning cycles. New foundation models, agent frameworks, industry-specific solutions, and commercial use cases emerge continuously.
To maintain leadership in AI-enabled customer engagement and commercial operations, U.S. Commercial requires a dedicated capability focused on separating meaningful innovation from short-term hype, identifying strategic opportunities early, and determining where to invest, experiment, partner, incubate, or scale.
This role ensures U.S. Commercial is not simply reacting to AI change but proactively shaping its future AI roadmap and external innovation agenda.
ROLE RESPONSIBILITIES
- U.S. Commercial AI Horizon Scanning
- Continuously monitor emerging AI technologies, vendors, startups, research, industry developments, and competitive activity relevant to U.S. Commercial.
- Track advances across generative AI, agentic systems, multimodal AI, intelligent automation, knowledge systems, customer engagement technology, commercial analytics, and AI infrastructure.
- Maintain a structured horizon-scanning framework that categorizes developments by strategic relevance, maturity, feasibility, and potential business impact.
- Produce concise executive updates on significant developments and their implications for U.S. Commercial.
- Identify signals that should influence future Commercial Engine, product, architecture, and transformation roadmaps.
- AI Innovation Assessment
- Evaluate emerging AI technologies and capabilities against U.S. Commercial priorities.
- Assess technical feasibility, scalability, differentiation, data requirements, integration considerations, and enterprise readiness.
- Distinguish meaningful commercial opportunities from short-term market hype.
- Create repeatable evaluation frameworks for AI products, vendors, models, platforms, and partnerships.
- Recommend whether opportunities should be monitored, tested, partnered, incubated, scaled, or retired.
- U.S. Commercial Opportunity Identification
- Identify emerging AI capabilities that can create value across customer engagement, marketing, market access, patient solutions, insights and analytics, commercial operations, field enablement, and the Commercial Engine.
- Translate external AI developments into potential U.S. Commercial use cases and future-state capabilities.
- Develop concept papers, opportunity briefs, and strategic recommendations for senior leaders.
- Partner with Product Management and Transformation teams to shape future investment and experimentation priorities.
- Connect emerging technologies to U.S. Commercial strategy and transformation roadmaps.
- External Innovation and Ecosystem Assessment
- Maintain intelligence on the external AI ecosystem, including startups, technology companies, academic institutions, research organizations, and industry innovators.
- Identify emerging capabilities that may warrant further evaluation or partnership.
- Lead the technical and strategic assessment of external capabilities for U.S. Commercial, including strategic fit, differentiation, feasibility, enterprise readiness, and potential value.
- Partner with the AI Partnerships & Ecosystem Lead to determine the appropriate engagement and partnership pathway.
- Provide clear recommendations on whether opportunities should be monitored, tested, partnered, incubated, scaled, or retired.
- Transition approved partnership opportunities to the AI Partnerships & Ecosystem Lead for engagement, coordination, contracting support, and ongoing relationship management.
- Monitor competitor AI developments and industry adoption trends.
- Maintain a prioritized pipeline of external technology opportunities aligned to U.S. Commercial strategy.
- Rapid Experimentation and Proof of Value
- Design and oversee focused proofs of concept for promising technologies and external capabilities.
- Define learning objectives, evaluation criteria, success measures, and time-bound decision points.
- Partner with AI Applications, Product, Data, and business teams to evaluate technical and commercial viability.
- Document findings, implications, and clear recommendations.
- Support recommendations to scale, partner, incubate, monitor, or retire opportunities, with partner engagement led by the AI Partnerships & Ecosystem Lead.
- Innovation Portfolio Management
- Maintain a transparent pipeline of emerging AI opportunities across near-term, emerging, and transformative horizons.
- Prioritize opportunities based on strategic value, commercial relevance, technical feasibility, scalability, differentiation, and risk.
- Apply a consistent intake and disposition model across external opportunities.
- Track progress from initial signal through evaluation, experimentation, partnership recommendation, roadmap integration, or closure.
- Provide portfolio insights that support U.S. Commercial investment decisions.
- AI Trend Analysis and Thought Leadership
- Synthesize research, market intelligence, industry developments, conference insights, and analyst perspectives into clear U.S. Commercial implications.
- Develop executive-ready materials that explain why developments matter, not only what has changed.
- Publish periodic AI outlooks and horizon-scanning updates.
- Serve as a credible internal thought leader on emerging AI capabilities and their commercial relevance.
- Help leaders distinguish durable opportunities from transient trends.
- Responsible AI and Enterprise Readiness
- Assess emerging technologies through security, privacy, legal, regulatory, compliance, quality, and Responsible AI lenses.
- Identify implementation constraints and risks early in the evaluation process.
- Partner with Governance, Risk, Architecture, Digital, and other relevant teams to evaluate enterprise readiness.
- Ensure recommendations account for Pfizer standards, technical dependencies, and responsible adoption requirements.
- Build risk and governance considerations into experimentation and partnership recommendations from the outset.
- Cross-Functional Partnership and Influence
- Partner with the Vice President, Applied AI & Architecture to translate external signals into future-state technical strategy and architecture.
- Partner with the AI Partnerships & Ecosystem Lead to connect broader external ecosystem activity with U.S. Commercial priorities and create clear handoffs for approved partner engagement.
- Partner with AI Applications and Product teams to create effective handoffs from exploration into product and engineering evaluation.
- Partner with Transformation & Delivery to connect validated opportunities with implementation and adoption pathways.
- Communicate complex technical and market developments clearly to business and executive audiences.
SUCCESS MEASURES
- Quality and relevance of horizon-scanning insights and strategic recommendations.
- External technology opportunities converted into pilots, partnership recommendations, product concepts, or roadmap priorities.
- Speed and rigor of opportunity evaluation and disposition.
- Adoption of recommendations in U.S. Commercial planning and investment decisions.
- Influence on future Commercial Engine and applied AI priorities.
- Quality of external capability assessments and partnership recommendations advanced to the AI Partnerships & Ecosystem Lead.
- Clarity and transparency of the innovation opportunity pipeline.
BASIC QUALIFICATIONS
- Bachelor’s degree in computer science, engineering, artificial intelligence, data science, business, or a related discipline, or equivalent professional experience.
- 8+ years of progressive experience in AI, technology strategy, innovation, product strategy, architecture, consulting, emerging technology, or related roles.
- Strong understanding of generative AI, agentic systems, machine learning, foundation models, and enterprise AI architectures.
- Experience evaluating emerging technologies and translating technical concepts into business opportunities and strategic recommendations.
- Demonstrated ability to monitor complex external landscapes, synthesize weak signals, and distinguish durable trends from hype.
- Experience developing executive-ready insights, business cases, opportunity briefs, or technology recommendations.
- Ability to communicate complex technical concepts clearly to senior business and technical leaders.
- Demonstrated success operating in ambiguous, fast-moving, and highly matrixed environments.
- Strong analytical, strategic-thinking, collaboration, and influencing skills.
PREFERRED QUALIFICATIONS
- Advanced degree in artificial intelligence, computer science, engineering, business, data science, or a related discipline.
- Experience in pharmaceuticals, healthcare, life sciences, or another highly regulated industry.
- Experience in innovation strategy, technology scouting, venture ecosystems, startup assessment, strategic partnership evaluation, or corporate development.
- Experience building AI roadmaps and evaluating AI investments or partnerships.
- Knowledge of U.S. Commercial pharmaceutical functions, customer engagement, marketing, market access, patient solutions, field enablement, or commercial operations.
- Strong knowledge of the AI, technology, research, academic, startup, and innovation ecosystems.
- Experience designing and evaluating rapid experiments or proofs of value.
RESOURCES MANAGED
Financial Accountability
Accountable for approved horizon-scanning, technology evaluation, and experimentation resources. External partnership budgets and ongoing partner management remain with the appropriate partnership owner. Specific budget responsibilities will be confirmed with Finance.
Supervision
May lead a small team of applied AI, innovation, or technology-scouting professionals and direct cross-functional evaluation teams, external experts, contractors, or project-based resources.
TRAVEL AND WORK REQUIREMENTS
- Travel anticipated based on business, conference, technology-evaluation, and team needs.
- Ability to work across global teams and time zones.
Candidate demonstrates a breadth of diverse leadership experiences and capabilities including: the ability to influence and collaborate with peers, develop and coach others, oversee and guide the work of other colleagues to achieve meaningful outcomes and create business impact.
Other Job Details:
- Last Date to Apply for Job: October 13, 2026.
- Ability to travel based on business needs
- This position is hybrid and requires working onsite 2 to 3 days per week
The annual base salary for this position ranges from $162,900.00 to $271,500.00. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 20.0% 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 $162K-$271K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. Disclosed range: $162K to $271K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Pfizer AI Hiring
Pfizer has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, New York, NY, Pearl River, NY. Compensation range: $271K - $271K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.