Bayesian Health, Inc. - Staff Machine Learning Engineer

United States3w ago

In Office Staff NA Cloud Computing Artificial Intelligence Machine Learning Engineer Python SQL MLOps MLflow AWS

Requirements

• 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. • 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. • Excitement for Bayesian’s mission and being a bar raiser so we can accelerate the pace at which we create value.

Responsibilities

• As a Staff Machine Learning Engineer, you are not satisfied with training and tuning ML models that predict clinical conditions in patients; you also want to own the effectiveness of your model in the real world. In practice, that means you aren’t afraid to get your hands dirty by writing data mapping code, debugging a specific patient case by following patient data as it moves through our AWS services, or improving the timeliness of your model’s predictions by reading and writing production-grade Python and SQL code. • 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. • Model Prototyping: • Productionizing: The same models that you develop with production-grade python. • Productionizing: • Deploying: Identify strategies for improving our production ML-based systems, and write, debug, and deploying production-grade Python code to implement those strategies. • Deploying: • MLOps: Build infrastructure that enables ML model development and deployment in production systems. • MLOps: