Machine Learning Engineer

Key Skills

Database, Agile methodologies, Machine learning techniques, Data science techniques, Deep Learning, Natural Language Processing (NLP), SPARK Programming, Databricks, MLOps tools, Natural Language Understanding-NLU

Job Description

We are seeking an intelligent, hard-working, and dynamic individual to serve as a Machine Learning Engineer within the AI Operations and Data Science Services group. The group encompasses expertise in AI, data science, machine learning, NLP, computer vision, production deployment, cloud infrastructure and data engineering and serves some of the most prominent research, clinical, and operations programs at the Institute. As we widen our support of several crucial centers and programs at DFCI (Dana Farber Cancer Institute), we seek an energetic and motivated engineer to help us expand and improve our portfolio of products and advance the overall mission of DFCI, which is to provide expert, compassionate care to children and adults with cancer while advancing the understanding, diagnosis, treatment, cure, and prevention of cancer and related diseases.

The Machine Learning Engineer will be responsible for enabling the achievement of machine learning objectives related to AI in Production, with significant overlap and collaboration with teams dedicated to radiology, pathology, and natural language processing (NLP). This is an applied ML (Machine Learning) role, and strong candidates would have knowledge of advanced methodologies in AI, including deep learning architectures in computer vision and NLP/NLU and self-supervised learning. The successful candidate will have proven experience in working on complex machine learning and AI problems, ideally in the healthcare space, and will have excellent oral, presentation, and written communication skills.

100% REMOTE is available as arrangement for this position.

Responsibilities

Key Responsibilities:

• Coordinate with AI in Production Functional Area Lead and Project Management to oversee and manage ML model backlog and Production AI portfolio
• Write SOPs (Standard Operating Procedures) and templates that facilitate translational ML efforts including automation and orchestration of ML lifecycle components.
• Develop and implement strategies to handle ML monitoring, including detection of model/data drift, model performance, and model consumption patterns.
• Contribute to ML-Ops (Machine Learning Operations) features on the Platform for Operationalized Data Science (PODS) product roadmap.
• Stay abreast of state-of-the-art AI/ML techniques through conferences, seminars, publications, newsletters, and other means of continuing education, and communicate such knowledge to coworkers, collaborators, and customers.
• Contribute to intellectual property, grant preparation, and research papers when appropriate.

Qualifications

Preferred Qualifications:

• MS or equivalent experience with evidence of impact in data science applied to real life problems in a research setting ideally within a clinical research environment
• 1 to 5 years of experience post MS or PhD, or relevant internship experience. 
• Experience working with healthcare (insurance, pharmaceutical, EHR, sensor, -omics, etc.) data at TB and PB scale.
• Knowledge of database schema design for both structured and unstructured databases and tradeoffs of utilization under various conditions.
• Strong programing skills and competency with Python, OOP, and common ML libraries (e.g., Spark, Databricks, Big Query etc.)
• A desire to work with healthcare and sincere appreciation for the legal, ethical, and privacy considerations regarding analysis of PHI (Protected Health Information) datasets.

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.