Owns end-to-end ML lifecycle from prototyping clinical prediction models to productionizing and deploying them using production-grade Python/SQL. Requires PhD +3yrs or Master's +5yrs experience with MLOps tools like SageMaker/MLFlow.
Salary not listed
RemoteML Engineering
About the role
Responsibilities
Model Prototyping: Develop and tune innovative, new ML models and labeler systems based on deep understanding of clinical use cases and state-of-the-art ML methods.
Productionizing: The same models that you develop with production-grade python.
Deploying: Identify strategies for improving our production ML-based systems, and write, debug, and deploying production-grade Python code to implement those strategies.
MLOps: Build infrastructure that enables ML model development and deployment in production systems.
Minimum Qualifications
Ph.D. in a relevant field plus 3+ years relevant experience, or a relevant Master’s degree and 5+ years experience shipping ML based software products.
Experience owning your ML models from prototyping to production.
Experience writing production-grade Python and SQL code to implement and evaluate ML models in production systems.
Experience using MLOps tools such as SageMaker and MLFlow.
Preferred Qualifications
Experience going 0-1 and shipping high impact AI/ML products.
Experience building solutions within healthcare and/or familiarity working with messy health data.
Experience working with enterprise customers, and the agility and responsiveness they require.
Comfortable interpreting / leveraging state-of-the-art peer-reviewed methods or tools in designing your approach.
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