Translational AI Engineer

Pfizer; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Pfizer is seeking a hands-on Translational AI Engineer embedded in the Research Unit to turn promising AI workflow concepts into durable, evaluated systems that accelerate translational science.

The role focuses on scaling prototypes, building data, evaluation, and deployment layers, and ensuring tools are reliable, reusable, and producible for scientific and clinical partners.

Qualifications

• PhD or Master’s with extensive ML engineering experience.
• Hands-on experience shipping AI/ML systems beyond prompt-wrapping.
• Strong Python engineer fluent in modern AI/ML tools.
• Experience reading and hardening AI-assisted codebases.
• Experience with AI/ML evaluation tooling and frameworks.

Responsibilities

• Design and build fit-for-purpose AI/ML systems for recurring scientific and clinical workflows, including hybrid RAG.
• Identify technical and scientific failure modes in AI prototypes and rebuild parts for scaling.
• Stand up lean data platform and ETL underneath these systems, in partnership with Digital teams.
• Own promotion of internal AI tools from alpha to beta and define production-readiness.
• Document SOPs to enable rigorous development from day one.
• Evaluate commercial AI/ML and GenAI tools against build-vs-buy criteria.
• Run evaluation loops against scientific/clinical benchmarks with collaborators.
• Distill learnings into robust primitives, architectures, and benchmark harnesses.
• Stay current on translational AI methods and infrastructure.

Skills

Python

AI/ML engineering

Model APIs

LangChain

PyTorch

HuggingFace

LlamaIndex

ETL pipelines

Education

PhD

Master's degree

Bachelor's degree

Tools

PyTorch

HuggingFace

LangChain

LlamaIndex

Job description

ROLE SUMMARY:

The technical engineering counterpart to AIDE's applied workflow roles, embedded within the Research Unit to convert promising AI workflow concepts into durable, evaluated, and supportable systems that accelerate translational science. This position is focused less on discovering use cases and more on the gap that appears once a prototype needs to scale in a scientific setting. The role translates recurring needs from computational biology, immunology, and clinical teams into fit-for-purpose AI systems, including generative AI, agentic workflows, predictive models, foundation models, retrieval-augmented systems, and fine-tuned model architectures. It also builds the data, evaluation, integration, and deployment layers beneath those systems, ensuring that internal AI tools do not remain fragile scripts or demos.

The ideal candidate is a hands-on translational AI engineer who can move fluidly between scientific intent, model behavior, and production-quality infrastructure. They should be comfortable reading and hardening AI-assisted codebases, standing up ETL (Extract, Transform, and Load) and database foundations, implementing cloud deployment and CI/CD patterns, and building evaluation harnesses that expose scientific, clinical, and technical failure modes before tools move from alpha to beta. The distinguishing strength is translational engineering judgment: turning ambiguous scientific needs into reliable AI systems, tracing failures to their root cause, defining fit-for-purpose evaluation with scientific and clinical partners, and knowing when to build internally versus adopt a commercial AI tool. This is a role for someone who turns promising AI workflows into systems that scientific and clinical partners can trust, reuse, and improve.

ROLE RESPONSIBILITIES:

• Design and build fit-for-purpose AI/ML systems for recurring scientific and clinical workflows, including hybrid RAG when grounding and domain context are required.
• Take AI-assisted prototypes built across AIDE and Systems Immunology, identify their technical and scientific failure modes, and rebuild parts that need engineering before scaling for wider use.
• Stand up lean data platform and ETL underneath these systems, in partnership with Digital, so tools do not "rot" into one-off scripts.
• Own promotion of internal AI tools from alpha to beta, define what "production-ready" means for a given tool, and build the evaluation harness that proves it.
• Establish and document the SOPs that help others build rigorously from day one, instead of discovering the gap after a tool is already widely used.
• Rigorously evaluate commercial AI/ML and GenAI tools and vendors against a build-vs-buy bar, covering capability, cost, security, scientific fit, and workflow readiness to support go/no-go purchasing decisions.
• Run evaluation loops that measure system quality against workflow-specific scientific or clinical benchmarks, using input from computational biologists, immunologists, biologists, and clinicians to drive model selection and iteration.
• Distill what is learned into hardened primitives, reference architectures, and benchmark harnesses that scale across AIDE's other tools and deployments.
• Apply the same rigor to AI that any scientific or translational method would receive: fit-for-purpose evaluation, grounded outputs, documentation, guardrails, disclosure of model limitations, and human oversight.
• Stay current on translational AI methods and infrastructure, including generative AI, agentic systems, predictive modeling, biological foundation models, retrieval and fine-tuning methods, and fit-for-purpose evaluation practices as they evolve.
• Contribute to a culture in AIDE where rigor is expected rather than exceptional, including calling out your own failures before someone else must.
• Build external presence by attending relevant conferences, publishing methods and evaluation results, and presenting materials that showcase the group's technical approach and impact.

BASIC QUALIFICATIONS:

• PhD with 1+ years of experience OR Master's degree with 5+ years of software or ML engineering experience - OR Bachelors degree and 6 + years of experience and/or equivalent demonstrated experience shipping AI/ML systems for scientific applications.
• Hands-on experience building AI/ML systems beyond prompt-wrapping, with depth in at least one modality such as generative AI, agentic workflows, predictive models, foundation models, hybrid RAG, or fine-tuned architectures.
• Demonstrated ability to evaluate, debug, and harden AI-assisted ("vibe-coded") codebases: read a prototype someone else wrote quickly with AI assistance, find its failure modes, and rebuild the parts that need real engineering.
• Strong Python engineer fluent in modern AI/ML tools, including model APIs, prompt engineering, hybrid RAG, fine-tuning, predictive modeling, evaluation tooling, and frameworks such as PyTorch, HuggingFace, LangChain, or LlamaIndex.
• In-depth database and ETL ex