3 days ago
Cambridge, MA, USA +2 moreStaff+
Base Salary
$139k - $232k/yr
Responsibilities
- Design, develop, and deploy production-grade AI systems supporting multimodal biomedical models and intelligent agents.
- Build data pipelines, retrieval systems, evaluation frameworks, knowledge systems, inference services, and human-in-the-loop workflows.
- Own architecture, implementation, testing, deployment, monitoring, operational support, and continuous improvement for assigned systems or platform capabilities.
- Translate scientific and business needs into scalable, maintainable, and reliable technical solutions.
- Partner with AI scientists, computational biologists, data engineers, and platform teams to turn research prototypes into production systems.
- Apply tradeoffs across performance, scalability, cost, reliability, security, privacy, maintainability, and delivery speed.
- Implement responsible AI practices involving evaluation, observability, reproducibility, lineage, security, and operational resilience.
- Contribute to architecture reviews, code reviews, engineering standards, reusable technical patterns, and technical mentorship.
- Define success metrics and operational indicators while improving system performance, reliability, usability, and scientific impact.
Requirements
- PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related technical discipline with 2+ years of relevant experience; or a master’s degree with 5+ years; or a bachelor’s degree with 6+ years.
- 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 high-performance computing environments.
- Strong Python software development skills and proficiency with modern machine learning frameworks such as PyTorch or JAX.
- Knowledge of 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 support 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 technical impact through deployed systems, publications, patents, open-source contributions, or reusable platforms.
Benefits
- Hybrid work requiring residence within commuting distance of a Pfizer location and onsite work an average of 2.5 days per week.
- Eligibility for Pfizer’s Global Performance Plan and share-based long-term incentive program.
- 401(k) with Pfizer matching contributions and an additional Pfizer retirement savings contribution.
- Paid vacation, holidays, personal days, caregiver/parental leave, and medical leave.
- Medical, prescription drug, dental, and vision coverage.
- Relocation assistance may be available based on business needs and eligibility.
Categories
AI ResearchML Engineering
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.