Translational AI Engineer

Biopharma Careers; Cambridge (MA); Hybrid; USD 139,000 - 232,000; Full time

Job summary

Pfizer seeks a senior AI/ML engineer to translate translational science needs into durable, scalable AI systems. You will design, implement, and evaluate production‑level AI tools and data platforms that support clinical and translational workflows, with emphasis on reliability, governance, and collaboration across biology teams.

The role requires hands‑on Python, ML engineering, and cloud deployment experience, with a track record of building robust evaluation harnesses and production‑ready

Qualifications

• PhD or Master’s with significant software/ML engineering experience in AI/ML systems for scientific applications.
• Hands‑on experience building AI/ML systems beyond prompt‑wrapping across multiple modalities.
• Ability to read prototypes quickly, identify failure modes, and rebuild with real engineering.

Responsibilities

• Design and build fit-for-purpose AI/ML systems for recurring scientific/workflows, including hybrid RAG.
• Debug and harden AI/ML prototypes to scale for broader use.
• Stand up data platform and ETL underneath systems with Digital for maintainability.
• Promote internal AI tools from alpha to production, defining production readiness and evaluation harnesses.
• Document SOPs to ensure rigorous development from day one.
• Evaluate commercial AI/ML tools vs internal builds for fit, cost, and security.
• Run evaluation loops against scientific/clinical benchmarks using stakeholder input.
• Distill learnings into reusable primitives, architectures, and benchmarks.

Skills

Python

AI/ML systems

Model APIs

Prompt engineering

Hybrid RAG

Fine-tuning

Predictive modeling

Evaluation tooling

Cloud deployment

Postgres or equivalent

AWS

GCP

Azure

CI/CD

LangChain

PyTorch

HuggingFace

LlamaIndex

Education

PhD with 1+ years experience

Master’s degree with 5+ years experience

Bachelor’s degree with 6+ years experience

Tools

PyTorch

HuggingFace

LangChain

LlamaIndex

Postgres

ETL tooling

AWS

CI/CD tooling

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.

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 experience (Postgres or equivalent) - able to stand up the data platform underneath a system, not only call an API.
• Fluency in cloud infrastructure and deployment: hands‑on with AWS, GCP, or Azure, containerization, and CI/CD.
• Sufficient immunology, biology, translational research, or clinical workflow literacy to understand the problems computational biologists, immunologists, biologists, and clinicians are trying to address.
• Demonstrated build‑vs‑buy judgment: has recommended adopting a commercial tool over an internal build where that was the better answer and can explain the tradeoffs that drove the decision.
• Demonstrated practice of building evaluation harnesses, error analysis, and hardening into systems from the start, with specific examples tied to scientific or clinical workflow requirements.
• Experience delivering in environments where requirements shifted as the work was learned, including examples of deliberately slowing down when speed was about to become a production liability.

PREFERRED QUALIFICATIONS

• Has taken an internal tool from “someone vibe‑coded this and it mostly works” to a hardened, evaluated system, and can tell that story with specifics, including what was broken.
• Has built and shipped a fit‑for‑purpose AI/ML system over messy scientific or clinical data, such as an agentic workflow, predictive model, foundation model application, hybrid RAG system, or fine‑tuned architecture.
• Has run a real build‑vs‑buy evaluation against commercial AI/ML or GenAI tools where the decision held up over time.
• Full‑stack range beyond the model layer: TypeScript and React, or equivalent, for internal dashboards and visual analytics that make evaluation results and system behavior legible to scientists and leadership.
• Experience in immunology, inflammation, or an adjacent therapeutic area within pharma or biotech R&D, or with multi‑omics data (RNA‑seq, proteomics, GWAS, spatial transcriptomics, single‑cell).
• Familiarity with biological foundation models such as scGPT, Geneformer, ESM, or AlphaFold, or their application to real immunology research problems.
• Has built production enterprise software meeting real security, compliance, auditability, and uptime requirements, not just a working prototype.
• Strong open‑source or publication record, ideally touching applied ML systems, evaluation methodology, or computational immunology.
• 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.

ADDITIONAL JOB DETAILS

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 [e-mail]. 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.