Applied AI and Machine Learning Scientist

Pfizer; United States; On-site; USD 150,000 - 230,000; Full time

Job summary

Pfizer is seeking an Applied AI/ML scientist to lead development of AI across the Internal Medicine Research Unit, translating advances in foundation models and multimodal AI into reusable capabilities that accelerate scientific discovery.

The role requires technical credibility across ML, biology, and drug discovery; you will shape the AI portfolio, govern evaluation standards, and partner with internal teams and external collaborators to deliver high-impact AI solutions.

Qualifications

• Advanced degree in a quantitative or scientific field.
• 5+ years in applied AI/ML leadership or similar roles.
• Experience with LLMs, foundation models, generative AI.
• Ability to translate scientific needs into reusable AI capabilities.

Responsibilities

• Provide AI/ML technical leadership across AIM2 by advancing LLMs, agentic systems, and multimodal AI.
• Lead evaluation and development of AI capabilities with scientific stakeholders.
• Guide design and evaluation of AI approaches ensuring methodological soundness and translational relevance.
• Develop reusable AI-enabled capabilities to strengthen scientific decision-making across IMRU.

Skills

LLMs Foundation Models

Agentic AI

Multimodal AI

Leadership

Python ML

Education

Advanced degree (CS/ML/biol)

Tools

TensorFlow

PyTorch

JAX

Job description

Role SummaryThe successful candidate for the Applied AI/ML scientist position leads the 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 discovery and decision-making. 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 scientist 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 identifying and advancing high-value use cases, leading the development and deployment of scientifically robust AI solutions 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 by advancing and applying large language models, agentic systems, multimodal AI, and related methods to high value scientific problems across Internal Medicine Research Unit (IMRU) while contributing technical expertise and recommendations that help shape the evolution of AI/ML capabilities in IMRU.
• Lead the technical evaluation and development of the AI capabilities within the AIM2, working with scientific stakeholders to identify and advance high-impact use cases where technically credible, reusable AI capabilities can create meaningful scientific or operational leverage.
• Serve as a senior technical and scientific leader across AIM2 Discovery Center, providing expert guidance on the design, evaluation and application of AI/ML approaches to ensure solutions are methodologically sound, fit for purpose, and grounded in biological, translational, and drug discovery context.
• Lead the design, development and application 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.
• Contribute technical expertise and best practices for the 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.
• Collaborate with external scientific and technology partners to evaluate external partnerships relevant to AIM2 priorities, providing technical expertise to 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 to stakeholders across IMRU, providing clear assessments of performance, applicability, limitations and opportunities for broader adoption.
• 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 5+ years with a Master's degree, 6+ years with a Bachelor's degree, or 1+ 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 developing innovative solutions and delivering measurable scientific, technical or operational impact.
• 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 and advance scientific applications of AI in ambiguous environments, translating scientific or stakeholder needs into practical, reusable solutions with measurable impact.
• Experience building and scaling