"Principal AI-Machine Learning Engineer"

The Principal 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 Principal 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.

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.

MINIMUM JOB QUALIFICATIONS:

Minimum Education:
Master’s degree required; PhD preferred.

Minimum Experience:
8 years of relevant experience required with Master’s degree or 7 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. Experience managing at least one person is preferred.

PRIMARY DUTIES AND RESPONSIBILITIES:

• Meeting and consulting with physician scientists requiring machine learning & AI support and designing plans and solutions, participating in their research activities, as well as supporting them in the grant writing, and leading research efforts when required.
• Interpreting complex machine learning & AI information. Summarizing and presenting results to senior leadership.
• Monitoring new developments in the field and has vision for staying at the cutting edge.
• Recognizing and reporting problems with the machine learning & AI process and identifying solutions.

KNOWLEDGE, SKILLS, AND ABILITIES REQUIRED:

• Strong team player with the ability to leverage the many excellent groups and resources at DFCI and Harvard more broadly and build collaborative strategies.
• Ability to communicate and solve problems effectively, track record in serving a variety of diverse customers and projects and of leading teams.
• Ability to work independently, prioritize, and manage people if needed, within an environment with rapidly changing priorities.
• Experience to teach three or more of the following:
  • Natural language processing or Computer vision technologies, Transformers, Adversarial / Generative models, JAX + Flex / Haiku, Vision Transformers, Federated learning, AutoML, Self-supervised learning, Causal ML, Reinforcement learning, Infrastructure as Code, DataOps (versioning, lineage, and governance), AIOps & MLOps life cycle (from deployment to monitoring to retirement), explainable AI, batch/online/streaming/edge training/inference, fully reproducible and auditable ML practices, CI/CD for large language models and large vision models, Multi-Cloud & Hybrid data platforms, productized Docker/Spark/Kubernetes solutions such as Databricks and Snowflake, High-throughput big data processing under redundancy / low-latency requirements.

SUPERVISORY RESPONSIBILITIES: None.

PATIENT CONTACT: None.