This role pays more than
57%
of similar roles. Most pay
$149,462–$214,000
— the shaded band above.
At the midpoint, this role pays about
$186k
versus about
$182k
for comparable roles.
Based on 240 similar postings.
Employer
About Pfizer
Pfizer Inc. is one of the world''s largest biopharmaceutical companies, researching, developing, manufacturing, and marketing medicines and vaccines across multiple therapeutic areas including oncology, cardiology, and infectious diseases. Industry: Biopharmaceuticals
Pfizer currently has
27 open roles
on FindRole.
Listed pay typically runs
$106,000–$176,600
across 27 roles with salary data.
The Translational AI Engineer joins the Research Unit to convert experimental AI workflow concepts into durable, evaluated, and supportable systems that accelerate translational science. This role focuses on bridging the gap between initial prototypes and production-ready tools for computational biology, immunology, and clinical teams. You will build and harden infrastructure including generative AI, agentic workflows, predictive models, foundation models, and retrieval-augmented systems. Key responsibilities include developing data platforms, managing ETL processes, implementing CI/CD patterns, and building evaluation harnesses to identify failure modes before moving from alpha to beta. The role requires proficiency in Python, PyTorch, HuggingFace, LangChain, or LlamaIndex, alongside experience with Postgres, cloud infrastructure like AWS or GCP, and containerization. You will solve the challenge of transforming ambiguous scientific requirements into reliable systems while navigating build-versus-buy decisions for various AI technologies.
Convert experimental AI prototypes into durable, production-ready systems for scientific and clinical workflows.
Build robust data infrastructure, including ETL pipelines and database foundations, to support scalable AI tools.
Develop evaluation harnesses to identify and mitigate technical and scientific failure modes before deployment.
Conduct build-vs-buy analyses of commercial AI tools based on cost, security, and scientific fit.
Establish standard operating procedures and reference architectures to ensure consistent development across the team.
Create hardened primitives and benchmark harnesses to standardize AI capabilities across multiple internal tools.
Maintain high standards for model safety by implementing guardrails, documentation, and human oversight.
Represent the organization's technical approach through conference attendance, publications, and public presentations.
What we're looking for
Must have a PhD with 1+ years experience, or a Master's with 5+ years of software/ML engineering experience, or a Bachelor's with 6+ years of experience.
Must possess hands-on experience building AI/ML systems including generative AI, agentic workflows, predictive models, and hybrid RAG.
Must be a strong Python engineer proficient in PyTorch, HuggingFace, LangChain, LlamaIndex, and other modern AI/ML tools.
Must have in-depth database and ETL experience with Postgres or equivalent to build data platforms rather than just calling APIs.
Must have fluency in cloud infrastructure (AWS, GCP, or Azure), containerization, and CI/CD deployment patterns.
Must possess sufficient immunology, biology, or clinical workflow literacy to collaborate with scientific teams.
Must demonstrate the ability to evaluate, debug, and harden AI-assisted codebases into production-ready systems.