Dana-Farber Cancer Institute is looking to solve the problem of revolutionizing the way the Institute conducts basic cancer research and provides best-in-class clinical oncology to patients by leveraging Artificial Intelligence and Machine Learning
Requirements
- Knowledge of MLOPS and ML engineering; GenAI and agentic AI; AL/ML and data science; data engineering and pipelines.
- Proficiency with Python, R, Databricks, and/or Snowflake.
- Experience with machine learning and AI
- Experience with data engineering and pipelines
- Experience with cloud environments
- Experience with CI/CD pipelines
- Experience with model deployment and maintenance
Responsibilities
- Plan, advise and execute on scalable practices for development, deployment, and long-term monitoring for AI solutions, ensuring a delivery that is built for scale, reliable telemetry tracking, informative monitoring and diagnosis of model drift, and cross-platform efficiency.
- Implement and maintain best-in-class data solutions, managing machine learning models from deployment to retirement, ensuring models are well organized, auditable, and continuously perform with the highest degree of accuracy.
- Develop CI/CD pipelines for models in cloud environments, including batch, online, streaming, and edge training/inference.
- Elicit functional requirements from end users and data science teams, utilizing methods such as user interviews, mockups, wireframes, end-to-end testing, prototypes, GUI designs, and use cases.
- Communicate status on various project/program efforts to multiple groups including the AIOS team, client-facing leads, project sponsors, business owners, and internal stakeholders.
- Mentor and provide guidance to junior and new team members.
Other
- Excellent communication and effective problem-solving skills
- Ability to work independently, prioritize, and manage people if needed, adapting to meet the evolving needs of the Institute
- Bachelor’s degree in a related field (Computer Science, Data Science, Engineering) required. Master’s degree preferred.
- 3 years of work experience in machine learning and AI required.
- Relevant lab work and research projects, teaching assistantships, internships, cooperative education programs undertaken during an advanced degree program may be considered toward qualifying work experience.
