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About This Role
POSITION SUMMARY
Vaccines Research is seeking a highly innovative and technically accomplished AI/ML Engineering leader to accelerate the transformation of scientific data into a strategic asset for AI\-driven vaccine discovery and development.
This role sits at the intersection of artificial intelligence, data engineering, and vaccine science. Embedded within Vaccines Research and supporting viral/bacterial vaccine programs, the successful candidate will lead implementation of a modern AI\-ready research data ecosystem that enables advanced analytics, predictive modeling, generative AI applications, and agentic scientific workflows.
Vaccine research generates exceptionally diverse data, including antigen and pathogen sequences, immunological assays, omics datasets, imaging data, laboratory workflows, electronic lab notebooks, and study metadata. The scientific value lies not within individual datasets, but in the ability to connect, contextualize, and operationalize information across these modalities.
You will partner closely with Scientists, Bioinformaticians, Digital teams, and enterprise stakeholders to establish data foundations that make research data discoverable, interoperable, reusable, and AI\-ready. These capabilities will power next\-generation predictive and translational models that inform vaccine design, candidate prioritization, and decision\-making across the research portfolio.
WHAT YOU"LL DO
Lead Research Data AI\-Readiness strategy and Implementation
- Drive implementation of Vaccines Research's strategy for transforming diverse research data assets into scalable, AI\-ready resources.
- Design and establish integrated data architectures that connect heterogeneous scientific datasets across laboratory, preclinical, and clinical domains.
- Develop automated data ingestion, transformation, and orchestration pipelines that convert fragmented research data into standardized, machine\-readable assets.
- Define and implement semantic data frameworks, metadata standards, ontologies, and knowledge representations that improve interoperability, discoverability, and reuse.
- Build and advance knowledge graphs, retrieval systems, and graph\-RAG capabilities that enable scientists and AI systems to interact effectively with both structured and unstructured research knowledge.
- Partner with enterprise data and digital organizations to ensure alignment with broader R\&D data standards, platforms, and AI initiatives.
Advance Predictive and Translational Modeling
- Develop and deploy machine learning approaches that leverage linked multimodal datasets to generate insights into vaccine\-induced immune responses and mechanisms of protection.
- Apply AI and predictive modeling techniques to support vaccine candidate evaluation, immunogenicity assessment, translational research, and portfolio decision\-making.
- Advance approaches that integrate preclinical, clinical, epidemiological, and real\-world datasets to improve scientific understanding and accelerate vaccine development.
Technical Leadership and Cross\-functional Influence
- Translate strategic AI objectives into scalable technical roadmaps, architectures, and implementation plans.
- Serve as a technical leader and trusted partner across immunology, microbiology, bioinformatics, clinical research, digital, and data science organizations.
- Identify opportunities to modernize research workflows through AI\-enabled automation and intelligent data integration.
- Act as a key liaison between Vaccines Research and broader Pfizer R\&D AI, data, and digital communities, ensuring vaccine\-specific needs are represented while leveraging enterprise capabilities whenever possible.
Advance AI Adoption and Scientific Innovation
- Evaluate and implement emerging AI technologies, including foundation models, agentic AI systems, multimodal learning approaches, and generative AI capabilities relevant to vaccine research.
- Mentor scientists and technical teams in AI best practices, responsible AI adoption, and data\-centric approaches to scientific discovery.
- Represent Vaccines Research in cross\-functional AI initiatives and contribute to shaping the future of AI\-enabled R\&D.
MINIMUM QUALIFICATIONS
- PhD in Computer Science, Machine Learning, Computational Biology, Software Engineering, AI, or a related discipline OR
- Master’s degree in Computer Science, Machine Learning, Computational Biology, Software Engineering, AI, or a related discipline and a minimum of 4 years of applied AI/ML experience in R\&D, Life Sciences or other related discovery focused environment
- Proven experience architecting and implementing data\-intensive AI/ML solutions utilizing complex scientific, biological, clinical, or real\-world datasets.
- Experience transforming heterogeneous research data into scalable and reusable data products, platforms, or AI\-ready ecosystems.
- Strong expertise in Python and modern AI/ML frameworks such as PyTorch, TensorFlow, or equivalent technologies.
- Experience designing data architectures, data integration frameworks, semantic data models, metadata standards, knowledge graphs, or related technologies.
- Experience working in cloud and/or high\-performance computing environments.
- Strong collaboration and communication skills with experience to working effectively across scientific, computational, and technology organizations.
- Experience influencing technical direction and driving initiatives across cross\-functional teams.
PREFERRED QUALIFICATIONS
- Experience with data standards/ontology frameworks common in life sciences (e.g., FAIR data principles).
- Experience working with immunology, systems biology, multi\-omics, flow cytometry, imaging, vaccine, or infectious disease datasets.
- Experience with generative AI, retrieval\-augmented generation (RAG), agentic AI, foundation models, or biological foundation models.
- Knowledge of translational modeling, biomarker development, clinical data science, or real\-world evidence applications.
- Record of scientific publications, patents, open\-source contributions, or recognized technical leadership in AI and life sciences.
WORK LOCATION ASSIGNMENT
- This is a hybrid role requiring you to live within commuting distance and work on\-site an average of 2\.5 days per week.
The annual base salary for this position ranges from $139,100\.00 to $231,900\.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.
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 $139K-$231K range is above 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 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($185K) sits 14% below the category median. Disclosed range: $139K to $231K.
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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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