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Role Summary
The successful candidate for the Applied AI / ML scientist position leads the technical evaluation, development, and application of AI across the Internal Medicine Research Unit (IMRU), translating advances in foundation models, agentic systems, multimodal AI, and related methods into reusable capabilities that strengthen scientific decision\-making end\-to\-end. The role combines deep technical credibility with strong scientific judgment and is accountable for shaping the AI portfolio within the newly created AI for IM Discovery (AIM2\) Discovery Center within the IMRU, defining governance and evaluation standards, assessing AI/ML capabilities in external (or internal) partnerships, and accelerating practical AI adoption across IMRU.
This role is intended for a technically credible AI leader who can operate at the interface of machine learning, computational biology, and drug discovery, while remaining grounded in the realities of scientific decision\-making. Success will require setting a clear strategy, directing high\-value use cases, ensuring that solutions and partnerships are scientifically robust and trusted, and building reusable capabilities that improve the speed, quality, and coherence of evidence generation across the portfolio.
Role responsibilities
- Provide AI/ML technical leadership for AIM2 and define a clear roadmap for how large language models, agentic systems, multimodal AI, and related methods will be applied to high\-value scientific problems across Internal Medicine Research Unit (IMRU).
- Lead the technical evaluation and development of the AI capabilities in the AIM2, identifying, prioritizing, and shaping opportunities so that AIM2 focuses on areas where technically credible, reusable AI capabilities can create meaningful scientific or operational leverage.
- Provide senior technical and scientific direction across AIM2 Discovery Center, ensuring that proposed solutions are methodologically sound, fit for purpose, and grounded in biological, translational, and drug discovery context.
- Guide the development of reusable AI\-enabled capabilities that strengthen scientific decision\-making end\-to\-end, with emphasis on scientific rigor, technical quality, reproducibility, and practical utility across IMRU lines.
- Establish governance and evaluation standards for AI\-built capabilities, including expectations for provenance, validation, guardrails, responsible use, and appropriate human oversight.
- Partner closely with IMRU Integrative Biology, IMRU line teams, MLCS, and Digital partners to ensure that AI efforts remain tightly aligned to real scientific needs and can be deployed in ways that are trusted, scalable, and adopted in day\-to\-day work.
- Shape and manage selected external partnerships relevant to AIM2 priorities, helping evaluate emerging technologies and collaborators while ensuring that external engagements remain aligned to Pfizer priorities and IMRU needs.
- Articulate the value and impact of the AI capabilities within IMRU to senior stakeholders, including technical differentiation, adoption trajectory, and return on investment of key initiatives to support strategic planning and decision making
- Build a strong technical culture within AIM2 and across IMRU, fostering scientific curiosity, high standards, collaboration, and continuous learning, while helping raise confidence in the responsible application of AI across IMRU.
BASIC QUALIFICATIONS
- Advanced degree in computer science, machine learning, artificial intelligence, computational biology, bioinformatics, statistics, engineering, life sciences, or a related quantitative or scientific field preferred.
- Typically, candidates at this level will bring substantial relevant experience, for example approximately 9\+ years with a Master’s degree, 10\+ years with a Bachelor’s degree, or 7\+ years with a PhD, while recognizing that the right mix of scope, technical depth, scientific credibility, and impact matters more than degree alone.
- Demonstrated experience leading complex, cross\-functional initiatives in applied AI, computational science, data science, digital transformation, or related domains, ideally with responsibility for strategy, portfolio prioritization, and value realization.
- Strong hands\-on understanding of LLMs, foundation models, generative AI, machine learning, and related AI approaches, with the technical credibility to guide decisions, assess trade\-offs, and challenge weak approaches even when not serving as the primary builder.
- Demonstrated ability to identify, prioritize, and shape high\-value use cases in ambiguous environments, translating scientific or stakeholder needs into practical, reusable solutions with measurable impact.
- Experience building and scaling reusable workflows, methods, products, or platforms rather than delivering isolated one\-off analyses.
- Demonstrated ability to develop strategy, shape AI portfolios, and communicate impact and return on investment to senior stakeholders in a clear and credible way.
- Strong matrix leadership, communication, and influence skills, including the ability to align senior stakeholders, provide technical and strategic direction, and drive adoption without relying solely on formal authority.
- Sound judgment regarding methodological rigor, evaluation, provenance, model limitations, risk, and the appropriate role of human oversight in AI\-enabled scientific workflows.
PREFERRED QUALIFICATIONS
- Experience in life sciences, pharma, biotech, translational science, omics, or related research environments.
- Experience and/or training in cardiovascular, metabolic, or obesity biology.
- Demonstrated ability to operate fluently across AI / technology and biology, grounding technical solutions in scientific reality and engaging credibly with scientists and line leaders.
- Experience with AI adoption, productization, governance, or workflow transformation in complex, matrixed, regulated organizations.
- Familiarity with scientific evidence synthesis, literature and document workflows, retrieval\-augmented approaches, multimodal AI, or agentic systems applied to scientific problems.
- Experience working with external technology partners, vendors, or academic collaborators to evaluate, shape, or deploy AI capabilities.
- Evidence of an entrepreneurial, product\-minded approach, including spotting opportunities, making pragmatic trade\-offs, iterating rapidly, and turning promising concepts into durable capabilities that are reused and adopted.
ORGANIZATIONAL RELATIONSHIPS
Director of AIM2; AIM2 Innovation Fellows; CSO IMRU; Head of IMRU Integrative Biology; Integrative Biology Scientists; IMRU biology line teams; Integrative Biology and Digital ecosystem partners; AI/ML practitioners, computational biologists, translational scientists, portfolio and strategy stakeholders, and other leaders involved in AI prioritization, deployment, governance, and adoption.
External:
May interact, as appropriate, with external technology partners, vendors, or academic / industry collaborators relevant to AI capability evaluation, development, and adoption.
*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.*
This is a hybrid role requiring you to live within commuting distance and work on\-site an average of 2\.5 days per week.
\#LI\-PFE
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.
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: $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. 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
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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