Overview
The Senior Artificial Intelligence & Machine Learning Engineer I/Scientist I works within the Artificial Intelligence Operations and Data Science Services group (AIOS) in the Informatics & Analytics department of Dana-Farber Cancer Institute – a teaching affiliate of Harvard Medical School.
The Senior Artificial Intelligence & Machine Learning Engineer I/Scientist I works in a team environment on both short-term priorities identified by our top clinicians, as well as on long-term institutional efforts that aim at revolutionizing the way the Institute conducts basic cancer research and provides best-in-class clinical oncology to our patients.
AIOS is part of the department serving some of the most prominent research and clinical programs at the Institute, from basic to translational research, to clinical deployment, and operationalization. The AIOS group encompasses expertise in AI, data science, machine learning, computer vision, NLP, production deployment, cloud infrastructure, data engineering, project management standards, and data labeling. Dana-Farber Cancer Institute (DFCI) provides expert, compassionate, and equitable care to children, adults, and their families, while advancing the understanding, diagnosis, treatment, cure, and prevention of cancer and related diseases. DFCI trains new generations of clinicians and scientists, disseminate innovative patient therapies and scientific discoveries around the world, and reduce the impact of cancer, while maintaining a focus on those communities who have been historically marginalized.
Dana-Farber Cancer Institute seeks a Senior AI/ML Engineer to lead the deployment, operationalization, and scaling of machine learning models and AI systems that support cancer research and clinical operations. The successful candidate will focus on building robust, production-grade ML infrastructure, automating end-to-end workflows, and integrating AI capabilities into DFCI’s cloud-based AI platform. The Senior AI/ML Engineer will be embedded in the AI Operations and Services (AIOS) within the departments of Informatics and Analytics, and work in close collaboration with research faculty, data engineers, and software engineers to build reproducible, scalable AI solutions.
Located in Boston and the surrounding communities, Dana-Farber Cancer Institute is a leader in life changing breakthroughs in cancer research and patient care. We are united in our mission of conquering cancer, HIV/AIDS and related diseases. We strive to create an inclusive, diverse, and equitable environment where we provide compassionate and comprehensive care to patients of all backgrounds, and design programs to promote public health particularly among high-risk and underserved populations. We conduct groundbreaking research that advances treatment, we educate tomorrow's physician/researchers, and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.
Responsibilities
Qualifications
Minimum Education:
Master’s degree required; PhD preferred.
Minimum Experience:
3 years of relevant experience required with Master’s degree or 2 years of relevant experience required with PhD. Deep machine learning & AI skills, at the interface with computer science. Python experience is required; R experience is a plus. Experience within a clinical or research environment preferred.
KNOWLEDGE, SKILLS, AND ABILITIES REQUIRED:
At Dana-Farber Cancer Institute, we work every day to create an innovative, caring, and inclusive environment where every patient, family, and staff member feels they belong. As relentless as we are in our mission to reduce the burden of cancer for all, we are equally committed to diversifying our faculty and staff. Cancer knows no boundaries and when it comes to hiring the most dedicated and diverse professionals, neither do we. If working in this kind of organization inspires you, we encourage you to apply.
Dana-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other characteristics protected by law.