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About This Role
ROLE SUMMARY
As Director, Applied AI, you lead the realization of AI strategy into tangible, production\-grade solutions, platforms, and products. You operate at the intersection of architecture, engineering, and product thinking — translating strategy, patterns, and standards into working systems deployed in real enterprise environments.
You own the end\-to\-end technical integrity of solutions — from concept through implementation — ensuring they are robust, scalable, and aligned with enterprise architecture and platform capabilities. You connect that build to business outcomes: you understand the commercial problem your work serves, not only the code. And you lead the people who build with you — full\-time engineers, contractors, and vendor resources whose day\-to\-day execution you direct and whose work you review to Pfizer's quality and security bar.
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. That depends on turning strategy into systems that actually work. This role ensures strategy and architecture become real, working solutions — deployable, integrated, and sustainable in production — and that the team and partners around you deliver to the same standard.
Candidate Profile
You are a hands\-on technical leader with strong execution capability and architectural judgment. You operate across disciplines — architecture, engineering, and product — and drive solutions from ambiguous beginnings to production reality. You are known for enforcing technical truth: ensuring what is built is correct, complete, scalable, and aligned with enterprise systems. You can frame a technical decision in terms of the business outcome it serves, and you can lead a mixed team of engineers, contractors, and vendors to ship it.
ROLE RESPONSIBILITIES
1\) Solution \& Platform Realization — Translate strategic intent and architectural patterns into working AI solutions and platforms. Own the end\-to\-end architecture and implementation shape of major solutions, ensuring they are production\-ready, scalable, maintainable, and aligned with enterprise architecture.
2\) Technical Leadership \& Hands\-on Execution — Lead technical design and execution across complex initiatives as architect, tech lead, and delivery driver. Contribute hands\-on code where it accelerates delivery and de\-risks execution, and guide teams through design decisions, implementation trade\-offs, and integration challenges.
3\) Integration Ownership — Ensure seamless integration across platforms and services, data systems, AI models, and business workflows — connecting models to real use cases, guardrails to runtime systems, and observability to operational feedback loops within enterprise constraints.
4\) Team \& Vendor Leadership — Lead and coordinate a delivery team of full\-time engineers, contractors, and vendor resources. Direct day\-to\-day technical execution, review their work to Pfizer's quality and security bar, manage dependencies and throughput, and grow the capability of the people you lead.
5\) Technical Quality \& Truth Enforcement — Identify and challenge incomplete or fragile architectures, solutions that are not production\-ready, and unrealistic assumptions. Enforce engineering discipline in scalability, observability, performance, and security so solutions meet enterprise quality and reliability standards.
6\) Business \& Domain Translation — Translate business problems into feasible, high\-impact technical solutions, and communicate solution direction in terms of business and commercial outcomes. Provide grounded technical input into domain\-level AI strategy.
QUALIFICATIONS
Basic Qualifications
- 10\+ years in software engineering, AI/ML, or architecture roles, with a strong delivery track record.
- Proven experience building production\-grade AI/ML systems or platforms.
- Current hands\-on coding ability and strong depth across at least 2–3 of: AI/ML and generative AI; system design / architecture; data engineering; cloud engineering.
- Demonstrated ability to lead complex technical initiatives end\-to\-end.
- Experience leading or coordinating engineers, contractors, and vendors to deliver production systems.
- Ability to connect technical decisions to business and commercial outcomes.
- Experience operating in cross\-functional, matrixed environments.
Preferred Qualifications
- Experience with LLMs, retrieval architectures, and AI agents; observability and monitoring; and AI governance and guardrails.
- Experience with Claude Code, Claude Enterprise, MCP development, and Claude Skill Development
- Prior role in biopharma TA role, epidemiology, or similar with hands\-on real\-world data experience
- Experience in enterprise\-scale or regulated environments.
- Experience leading platform or large solution\-development efforts.
- Familiarity with CI/CD, DevOps, and production operations.
ORGANIZATIONAL RELATIONSHIPS
Senior Director, Applied AI; product management, engineering, architecture, and delivery teams; program leaders; vendor and contractor partners.
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.
PHYSICAL/MENTAL REQUIREMENTS
Sitting, standing, walking, bending, ability to perform mathematical calculations and ability to perform complex data analysis on a computer
NON\-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS
10\-20% travel
Additional Job Information:
Last Date to Apply: June 19, 2026
Work Location Assignment: Hybrid
The annual base salary for this position ranges from $176,600\.00 to $294,300\.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 $176K-$294K 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 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 $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 ($235K) sits 32% above the category median. Disclosed range: $176K to $294K.
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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