Interested in this AI/ML Engineer role at Pfizer?
Apply Now →About This Role
At Pfizer, our mission is to create breakthroughs that change patients’ lives, and the company just rebuilt its entire technology operating model to deliver on it. Pfizer is becoming an AI\-native, agentic\-supported organization: enterprise AI platforms in the hands of all 80,000 colleagues, decisions moved closest to the work, and a deeper level of federation than most industry peers. For a designer, this is a rare moment: a 177\-year\-old company rewiring itself around AI, feedback loops measured in days, and design decisions that shape how an entire company works and how medicines reach patients. We are looking for a Senior Manager, AI Experience Designer to shape how those platforms look, sound, and behave.
The Senior Manager, AI Experience Designer drives products from inside the team: sitting with engineers and product owners daily, shaping scope and priorities with them, and pushing concepts to working proofs in days rather than weeks. What makes this role different is what it owns: pattern areas of Pfizer’s AI\-first design system, shipped as code into the build pipeline so the correct, compliant pattern is what every team gets by default. Success is measured the way the platforms are measured: pattern reuse, first\-pass compliance, and experiences colleagues actually adopt.
Design is changing fast, and this role stays ahead of it: exploring new tools and methods first, testing them on real work, and bringing what works back into the system. The role keeps the reference library behind Pfizer’s automated design quality checks, watches how patterns perform in the wild, and retires the ones that stop earning their place. A strong technical background is required: the role builds with LLM APIs and agent frameworks to prototype real agent behavior, speaks the language of engineers, and holds their own in product and architecture conversations.
This is an individual contributor role with real authority over the work: the Senior Manager, AI Experience Designer delivers the work personally and owns the outcome. This role specifically requires experience designing Agentic AI and Conversational UX (LLM\-enabled assistants): how AI systems behave, communicate, and act, balancing autonomy, human\-in\-the\-loop controls, transparency, safety, and trust.
ROLE RESPONSIBILITIES
Design System Pattern Ownership: Own specific pattern areas of Pfizer’s AI\-first design system (assistant UI components, conversation templates, transparency and disclosure components, escalation patterns, trust and verification cues) as products with adoption targets.
Design Standards as Code: Ship design tokens, components, and patterns into the engineering build pipeline and MCP so the correct, compliant pattern is the default for every team.
Design QA Reference Library: Maintain the reference library behind automated design quality checks: the voice patterns, disclosure components, and trust cues the Design QA Agent enforces.
Pattern Adoption \& Standards Drift: Monitor pattern usage, override rate, and exception requests for owned surfaces; retire or redesign patterns the data shows are not working.
Agent Prototyping in Code: Build with LLM APIs and agent frameworks to prototype real agent behavior rather than static screens, and validate workflows and risks before build.
Agentic \& Conversational Design: Lead the design of Agentic AI experiences where systems can plan, make decisions, call tools/functions, and complete multi\-step workflows with humans in the loop. Own Conversational UX end\-to\-end (chat and/or voice): dialog flows, conversation state, error recovery, escalation to human support, and safe fallback behaviors.
Assistant Behavior Design: Design and document assistant behaviors: tone and voice, grounded responses, refusal patterns, transparency cues, and trust\-building interaction patterns.
Hands\-On Design Partnership: Be a vocal, engaged, proactive, and hands\-on partner within a cross\-functional team, shaping scope and priorities with engineers and product owners daily.
Rapid Delivery: Deliver end\-to\-end design for high\-impact projects under tight deadlines, ensuring alignment with business goals, user needs, and technical feasibility; produce MVPs and proofs of concept that validate business ideas within days.
Accessibility \& Inclusive Design: Ensure accessibility compliance (WCAG 2\.1 AA) across all designs, championing inclusive design practices for diverse user needs.
Playbooks \& Pattern Libraries: Create reusable playbooks and pattern libraries for agentic workflows and conversational design (e.g., escalation, repair, safety responses, approvals, and multi\-step task orchestration).
Enablement \& Peer Mentorship: Proactively train and mentor designers and cross\-functional partners on the rapid prototyping process, AI tools, and internal design systems.
Product\-Minded Design Strategy: Act as a product\-minded design strategist: frame problems, define hypotheses, influence roadmaps with Product partners, and connect design decisions to measurable outcomes.
ROLE BEHAVIOURS
Don't Lose Your Curiosity: Experiments constantly with new AI design tools and methods on real work; shares what works and keeps their own practice current as the craft changes.
Act with Agency: Gets just enough context and starts making; improvises around missing inputs, acts on partial information, and corrects course through the work itself rather than waiting for a plan. Pairs that bias to build with care for the craft: a strong sense of style, sweated interaction detail, and taste grounded in systems design.
Set an Example for Your Team: Models craft and pace for peers; the quality and speed of their own shipped work sets the standard others reference.
Own the Outcome: Takes accountability for the adoption and quality of the surfaces and patterns they own; follows their work past release to confirm it performs.
Be Polymath Oriented: Combines interaction and conversation design with working technical knowledge of LLMs and agents; moves comfortably between design, product, and engineering contexts.
Communicate with Precision: Writes specs and documentation engineers can build from directly; presents design rationale clearly to product and engineering partners.
Think in Systems: Designs patterns for reuse, considering how each component behaves across products and agent surfaces rather than on a single screen.
BASIC QUALIFICATIONS
- Bachelor’s degree with at least 6\+ years of experience; OR a Master’s degree with more than 5\+ years of experience
- 6\-10 years of progressive UX design experience, with a proven track record in delivering high\-impact digital products and experiences.
- Working fluency with front\-end development principles, design tokens, and APIs, sufficient to ship design system components into engineering pipelines.
- Hands\-on experience building with LLM APIs or agent frameworks to prototype real agent behavior in code, not only static interfaces.
- Experience shipping design system components and patterns into production engineering pipelines (design tokens, documented components, versioned patterns).
- Demonstrate experience designing Conversational UX (chat and/or voice), including dialog flows, conversation state, repair/fallback patterns, escalation, and tone/voice guidelines.
- Demonstrate experience designing Agentic AI workflows (systems that can take actions via tools/functions), including human\-in\-the\-loop controls, approvals, transparency, and safe failure modes.
- Extensive experience working with AI\-driven design tools and platforms, with a deep understanding of how to leverage them for rapid prototyping and iterative design.
- "AI\-first" mindset with a proven willingness to challenge traditional design paradigms and adopt augmented workflows to radically accelerate delivery cycles.
- Expertise in accessibility and inclusive design, with hands\-on experience ensuring WCAG 2\.1 AA compliance across a wide variety of digital platforms.
- Proven ability to deliver complex design projects under tight deadlines with a focus on speed, efficiency, and quality.
- Strong communication skills, including the ability to present and justify design decisions to product, engineering, and business partners, and to senior leadership in partnership with design leadership on executive presentations.
- A robust portfolio demonstrating system\-level design thinking, AI\-powered experiences, and rapid prototyping techniques, including design system components or patterns 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.
Expertise in working within lean or agile environments with fast\-paced, iterative product cycles.
Previous experience designing within the healthcare or pharmaceutical sectors is a plus.
Experience defining responsible AI UX patterns: transparency/disclosures, explainability cues, privacy boundaries, and trust/safety guardrails.
Experience with AI experience evaluation approaches: conversation quality testing, scenario\-based risk testing, and iterative improvement loops tied to product metrics.
Strong knowledge of service design, journey mapping, or customer experience frameworks.
ADVANCED\-LEVEL SKILLS
Craft Depth: Produces high\-quality interaction and conversation design end\-to\-end, from ambiguous input to shippable patterns, under tight timelines.
Design\-Engineering Collaboration: Ships design tokens and components into engineering pipelines; understands front\-end and API constraints well enough to design within them.
Applied AI Fluency: Working knowledge of how LLMs and agentic systems behave, applied directly in prototypes and pattern design.
Clear Communication: Presents and defends design decisions with product and engineering partners; produces documentation strong enough to scale beyond their own surfaces.
Problem Framing: Turns loosely defined requests into concrete design problems and validated prototypes.
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 $124,400\.00 to $207,400\.00\.\* In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 17\.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. \* The annual base salary for this position in Tampa, FL ranges from $111,900\.00 to $186,500\.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 $111K-$207K range is below the median 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($159K) sits 26% below the category median. Disclosed range: $111K to $207K.
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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