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About This Role
ROLE SUMMARY
At Pfizer, our mission is to create breakthroughs that change patients’ lives. As part of the Customer Experience function in the Chief Technologist Organization, we are seeking a Director, AI Experience Design Lead to own how Pfizer’s AI looks, sounds, and behaves, and to build and lead the design team that ships it.
Pfizer is putting AI in front of 80,000 colleagues and every surface needs to feel like it came from the same company. This role owns that consistency as a product: the AI\-first design system behind human and agent experiences. It defines what colleagues see, what agents say, how uncertainty gets disclosed, and how regulated content behaves. The deliverables are working systems: tokens, patterns, and standards that live in MCP and flow into the build pipeline, so engineers and the Design QA Agent get the correct pattern by default and teams build compliant experiences without waiting on review cycles.
The Director is accountable for the system the way a product owner is accountable for a product: adoption in code, pattern reuse, override rates, and first\-pass compliance are the scoreboard. When telemetry shows a pattern failing, the Director retires it.
This role is designed for a highly experienced design leader who brings exceptional executive presence, narrative storytelling ability, and political acumen, while remaining deeply hands\-on and invested in growing the people on their team: a player\-coach who ships production design work alongside the team and leads complex, high\-stakes design initiatives across Pfizer’s AI platforms and enterprise LLM integrations.
This is a Director\-level role combining people leadership with deep individual contribution. The Director works across every modality the platforms speak (screens, conversation, voice, and agent\-to\-agent interaction) and moves fluidly between shipping production design work, delivering design standards as code, and growing the team. Deep technical understanding of how LLMs, agents, and modern product stacks behave anchors the role’s credibility with Engineering and Product.
ROLE RESPONSIBILITIES
AI Design System Ownership: Own, extend, and evolve Pfizer’s AI\-first design system across human and agent surfaces: the single source of truth for how Pfizer’s AI looks, sounds, and behaves.
Design Standards as Code: Deliver design standards as code: tokens, patterns, and components shipped into the engineering build pipeline so the correct, compliant pattern is the default, with accessibility (WCAG 2\.1 AA) built in.
Adoption Tracking \& Standards Drift: Track adoption in code (pattern usage, override rate, exception requests) and retire patterns the data shows are not working.
Experience Metrics: Connect design outcomes to measurable KPIs: adoption, task success, trust, time\-to\-resolution, and first\-pass compliance.
Lead by Example \& Innovation: Set the design bar by shipping: work in the new AI tools first, put innovative work on the table, and show the team how the craft is done now.
Hands\-On Design Delivery: Independently own end\-to\-end design execution for high\-impact, ambiguous initiatives, from problem framing through high\-fidelity, interactive, and executive\-ready prototypes.
Agentic \& Conversational Design Authority: Act as the design authority for Agentic AI experiences, including systems that plan, reason, invoke tools, and execute multi\-step workflows with humans in the loop. Own Conversational UX end\-to\-end (chat and/or voice), including dialog flows, conversation state, error recovery, escalation, and safe fallback behaviors.
Assistant Behavior Design: Define assistant behaviors such as tone and voice, refusal patterns, transparency cues, and explainability, designing for edge cases and failure modes in regulated contexts.
LLM Product Partnership: Partner closely with AI, Data, Product, and Engineering teams to co\-own LLM\-enabled products that balance autonomy, usability, safety, and trust.
Craft Excellence: Maintain an exceptional design bar across interaction design, visual design, systems thinking, product thinking, and storytelling.
Executive Design Advisory: Serve as a trusted design advisor to Product, Engineering, and Executive stakeholders across the Chief Technologist Organization’s platform teams and translate complex design and AI concepts into decisions leaders can act on.
Product Vision \& Roadmap: Support product vision and roadmap prioritization, helping Product and Engineering partners evaluate which AI capabilities to build, sequence, or sunset based on user value and experience impact.
Team Building \& Talent Lifecycle: Build, lead, and grow a high\-performing team of experience designers: hiring bar, roles and levels, growth paths, and the full talent lifecycle; mentor direct reports by modeling the craft in their own work.
Budget \& Resourcing: Own the team’s operating budget and staffing across platforms and initiatives; partner with Finance on annual planning.
ROLE BEHAVIOURS
Don't Lose Your Curiosity: Leads the practice’s exploration of new design tools and methods; experiments in the open and brings validated techniques into the team’s standard ways of working.
Set an Example for Your Team: Models the craft daily; ships production design work that sets the bar for the team and shows, rather than tells, how AI\-first design gets done.
Act with Agency: Makes decisions without waiting for a laid\-out plan; gathers just enough context and starts making, improvises around missing inputs, and corrects course with evidence from the work itself.
Care for the Craft: Holds a strong, defensible point of view on visual and interaction style; treats typography, motion, tone, and interaction detail as the product rather than the polish.
Own the Outcome: Takes accountability for experience quality and adoption on every surface the design system touches; treats pattern reuse and first\-pass compliance as the team’s scoreboard.
Be Polymath Oriented: Works fluently across design, engineering, and AI; translates between design intent and technical implementation and builds the team’s depth beyond a single specialty.
Communicate with Precision: Sets the standard for spec\-driven design communication; presents design rationale clearly to audiences from engineers to senior leadership.
Think in Systems: Designs patterns once so they scale across every platform and agent surface; connects design decisions to how the whole experience, organization, and platform behave together.
BASIC QUALIFICATIONS
- Bachelor’s Degree required; MBA or advanced degree in a related field preferred.
- 8\+ years of progressive experience in Experience Design, UX, or Product Design, with a proven track record of delivering complex, high\-impact digital products and experiences in enterprise or highly matrixed environments.
- 5\+ years of direct people management experience, including hiring, developing, and leading high\-performing design teams.
- Working fluency with front\-end development principles, APIs, and design\-token pipelines, sufficient to partner with Engineering on delivering design standards as code.
- Experience owning and evolving design systems and AI\-first interaction patterns at scale to ensure scalability, consistency, and quality.
- Deep expertise in AI\-driven experience design, including hands\-on experience designing Agentic AI workflows and Conversational UX (chat and/or voice), such as dialog flows, conversation state management, repair and fallback patterns, escalation models, and human\-in\-the\-loop controls.
- Advanced understanding of how AI systems behave, communicate, and build trust, including experience designing transparency cues, explainability patterns, refusal behaviors, safety guardrails, and responsible AI interaction models.
- Exceptional design judgment and craft excellence, with the ability to independently produce end\-to\-end design work from problem framing through high\-fidelity, interactive, and executive\-ready prototypes.
- Outstanding executive communication and storytelling skills, including the ability to clearly articulate, justify, and defend design decisions to senior leadership and diverse stakeholder groups.
- Expertise in accessibility and inclusive design, with hands\-on experience ensuring WCAG 2\.1 AA compliance across digital products and platforms.
- Strong analytical and problem\-solving skills, with the ability to synthesize qualitative and quantitative inputs into clear design direction and actionable insights.
- A robust portfolio demonstrating system\-level thinking, AI\-powered experiences, and rapid prototyping approaches, including design standards or components shipped as production code.
PREFERRED QUALIFICATIONS
Strong product design experience (not only UI): partnering with Product/Engineering to shape scope, prioritize tradeoffs, and define measurable outcomes.
Hands\-on building with LLM APIs or agent frameworks to prototype real agent behavior in code, not only static interfaces.
Advanced prompt engineering skills, using prompts as detailed creative briefs that set parameters, constraints, and brand identity.
Expertise in working within lean or agile environments with fast\-paced, iterative product cycles.
Track record of building design teams from the ground up, including hiring, onboarding, and career\-pathing frameworks.
Experience with AI experience evaluation approaches: conversation quality testing, scenario\-based risk testing, and iterative improvement loops tied to product metrics.
EXPERT\-LEVEL SKILLS
Craft Mastery: A recognized authority in interaction, visual, and conversation design; independently produces work from ambiguous brief to production\-ready execution.
Design\-Engineering Fluency: Operates where design and engineering converge; ships tokens, patterns, and standards as code and engages engineers on implementation tradeoffs.
Agentic AI Expertise: Deep working knowledge of how LLMs and agentic systems behave; designs trustworthy, transparent, and explainable experiences and anticipates failure modes.
Multi\-Audience Communication: Communicates design rationale from engineering standups to executive forums and translates complex AI and design concepts into decisions leaders can act on.
Problem Discovery: Turns ambiguous, high\-stakes briefs into clear design problems; the go\-to person for the least defined design challenges on Pfizer’s AI platforms.
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
- Strong digital literacy and an "AI\-first" mental model, with the ability to critically evaluate and integrate generative tools into the creative process to solve complex design challenges at scale
- Ability to manage multiple complex programs and projects simultaneously
- Strong analytical and problem\-solving skills
NON\-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS
- Occasional travel may be required for workshops, team meetings, or stakeholder engagement
- Flexibility to work across global time zones as needed
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.
Bus Dev \& Strategic Planning
Salary Context
This $176K-$294K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($235K) sits 10% above the category median. Disclosed range: $176K to $294K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Pfizer AI Hiring
Pfizer has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Pearl River, NY, US, Cambridge, MA, US, New York, NY, US. Compensation range: $207K - $358K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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