3 days ago
Cambridge, MA, USA +2 moreSenior
Base Salary
$106k - $177k/yr
Responsibilities
- Own an end-to-end AI subsystem, platform capability, or workflow from architecture through production and operational support.
- Design, develop, deploy, and maintain production-grade AI systems supporting multimodal biomedical models and intelligent agents.
- Engineer data pipelines, retrieval systems, evaluation frameworks, knowledge systems, inference services, and human-in-the-loop workflows.
- Translate scientific and business needs into scalable, reliable, secure, maintainable technical solutions.
- Lead architecture, implementation, testing, deployment, monitoring, troubleshooting, and continuous improvement.
- Implement practices supporting model evaluation, observability, reproducibility, lineage, security, operational resilience, and responsible AI.
- Define success metrics and operational indicators and continuously improve performance, reliability, usability, and scientific impact.
- Contribute to architecture reviews, code reviews, engineering standards, and reusable technical patterns.
- Mentor and support less-experienced engineers through collaboration, code reviews, and technical coaching.
Requirements
- PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related technical discipline with demonstrated experience building software, systems, or machine learning solutions; or a master's degree with 2+ years of relevant experience; or a bachelor's degree with 4+ years of relevant experience.
- Demonstrated experience designing and delivering production software or machine learning systems and owning technical solutions from architecture through deployment.
- Experience building reliable systems in cloud, distributed computing, or HPC environments.
- Strong Python software development proficiency and experience with modern machine learning frameworks such as PyTorch or JAX.
- Proficiency in software architecture, system design, APIs, testing, debugging, version control, technical documentation, containers, orchestration, and deployment workflows.
- Depth in one or more of distributed GPU training, model serving and inference optimization, AI platforms and MLOps, data and knowledge systems, LLM and agentic architectures, retrieval and RAG, AI evaluation frameworks, or performance and infrastructure optimization.
- Ability to work across interdisciplinary teams, navigate ambiguity, communicate technical concepts broadly, influence decisions, and develop less-experienced teammates.
- Preferred qualifications include experience in life sciences, healthcare, biotechnology, or regulated industries; multimodal AI, scientific machine learning, computational biology, or cheminformatics; responsible AI and governance; and evidence of technical impact through deployed systems, publications, patents, open-source contributions, or reusable engineering platforms.
Benefits
- Hybrid work arrangement requiring residence within commuting distance of a Pfizer location and onsite work an average of 2.5 days per week.
- Comprehensive benefits including a 401(k) plan with Pfizer matching contributions, an additional retirement savings contribution, paid vacation, holidays and personal days, paid caregiver/parental and medical leave, and medical, prescription drug, dental, and vision coverage.
- Eligibility for Pfizer's Global Performance Plan bonus and share-based long-term incentive program.
- Relocation assistance may be available based on business needs and eligibility.
Categories
About Pfizer
Pfizer is a publicly traded biopharmaceutical company that discovers, develops, manufactures, and markets prescription medicines and vaccines for patients, healthcare systems, and governments worldwide. Its portfolio spans oncology, vaccines, immunology, and rare disease, with products such as the COVID-19 vaccine (with BioNTech) and the antiviral Paxlovid. Founded in 1849 and headquartered in New York City, Pfizer operates globally under the ticker PFE on the NYSE.