Interested in this AI/ML Engineer role at Pfizer?
Apply Now →About This Role
Use Your Power for Purpose
As Director, you turn AI transformation ambition into adoption that sticks. You own making change real: operationalizing the business case, the outcomes, the business teams you serve, the partnerships, and the hybrid team of people and agents that delivers. Beyond standing the work up, you move people through it. You are strategic enough to shape where this goes and hands\-on enough to build it and land it yourself. You partner closely with the technical leads, and you bring the change and adoption discipline that turns what gets built into what gets used.
Why This Role Matters
Complex AI work moves only as fast as its weakest coordination point, and it creates value only when people adopt it. As part of how Pfizer turns its AI ambition into delivered outcomes, you create the operational and change discipline that lets a senior technical team move at pace without losing control, and without losing the business teams it serves. You sit between strategy, execution, and the people whose ways of working are changing, and you are accountable across all three.
Candidate Profile
You are a change leader who is strategic but still produces. You have owned outcomes and shaped executive\-level adoption strategies, and you partner with cross\-functional technical teams you do not directly manage. You can hold a room of senior leaders and engineering stakeholders, and you know how to bring people along through significant change. You have deep domain instinct, real AI fluency, and a feel for the human side of transformation.
What You Will Achieve
- Vision \& Strategy: Know the commercial strategy well enough to judge which problems are worth solving, set the direction for the initiative, and keep the work anchored to the outcomes the business cares about. Build the change narrative that makes that direction land with the people it affects.
- AI\-First Leadership: Lead this as an AI\-first effort, not a conventional program with AI added on top. Push the team to reach for AI where it changes the shape of the work, hold automation and intelligence as the default rather than the exception, and set ways of working (for people and agents alike) that the rest of the portfolio can learn from.
- Change \& Adoption: Own how the business moves from current state to new ways of working. Read stakeholder readiness, surface and work through resistance, build the enablement and communications that make the change usable, and design for sustainment so the gains hold after launch. Treat adoption as a measured outcome, not a hope.
- Business Outcomes: Own the result and the work that gets there. Tie the initiative to commercial outcomes and to the adoption that drives them and move measurably on both. When something is not working, you are close enough to the work and the people to catch it and change course.
- Hybrid Operating Model: Understand the shifts are part\-people, part\-agents model asks of an organization, and the new dynamics leaders face as they start leading hybrid teams. Help shape the operating model (what humans do, what agents do, and how they work together) and bring the change discipline that helps people adapt to it.
- Program \& Delivery Management: Own the initiative plan and key milestones. Drive cadence and ceremonies, track progress against goals (including adoption and change milestones, not only delivery), manage dependencies and critical path, and keep delivery on schedule.
- Reporting \& Communication: Produce clear, honest status for the VP, business sponsor, and governance forums. Maintain the single source of truth on status, decisions, risks, and adoption health.
Here Is What You Need (Minimum Requirements)
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- Bachelor’s degree with at least 8\+ years of experience; or a Master’s degree with more than 7\+ years of experience; or a Ph.D. with 5\+ years of experience.
- Experience leading change management and transformation on complex, multi\-stakeholder programs, ideally where new technology shifted how people work.
- Experience owning outcomes and shaping executive\-level adoption strategies, with a track record of working effectively alongside cross\-functional technical teams you do not directly manage.
- Experience with program or product management, delivery, or transformation.
- Experience building a business case and operating credibly with executive\-level leadership.
- Working fluency in AI and technical delivery, able to engage credibly with engineers on what is being built and why.
- Experience coordinating vendors, contractors, and internal teams against scope, schedule, and budget.
- Strong facilitation, stakeholder management, and change communication, including with senior executives.
- Excellent written and verbal communication, with the ability to make complexity legible and to make change feel doable.
- 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 Day to Apply:June 25, 2026
- Work Location Assignment: Hybrid, 2\-3 days onsite/week US Commercial Pfizer site (per Pfizer’s Log in for Your Day Policy)
- Eligible for employee referral bonus
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. \* The annual base salary for this position in Tampa, FL ranges from $146,600\.00 to $244,400\.00\. 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 $146K-$271K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($209K) sits 15% above the category median. Disclosed range: $146K to $271K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Pfizer AI Hiring
Pfizer has 8 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Positions span New York, NY, US, Remote, US, Cambridge, MA, US. Compensation range: $176K - $456K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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