Machine Learning Engineer II

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Project Examples

• ML Platform Improvements. You will play a critical role building tools and infrastructure to support Included Health’s mission to improve healthcare for everyone. A key focus area will involve reshaping and extending a PySpark-based ML platform. The tooling developed will support a combination of modeling, experimentation, and measurement capabilities to build an understanding of the clinical needs of our members and the efficacy of interventions. You will work to ensure robust modeling and data delivery systems that operate at the required scale and velocity. Here you’ll leverage your past experience with PySpark and ML packages like scikit-learn. 
• Expanding Our Feature Store. To accelerate machine learning efforts and simplify analyses, we’re expanding our current feature store. This work will include understanding developers’ needs, implementing developer-friendly interfaces, and implementing efficient feature retrieval tools.  
• Productionizing Services. We’re aiming to bring more data science to our product. For example, making personalized recommendations to our members. Machine learning engineering will help bring these models into production, be responsible for their incident-free operation, and streamline the process of putting future models into production. You’ll use your experience with API development, integration tests, tools like Docker, and platforms like AWS to help get data science into our products. You’ll become more familiar with Kubernetes and GraphQL.

What We Look For...

• 3+ years of production coding experience and comfort with an on-call rotation
• Previous experience in machine learning and statistics fundamentals
• A history of developing production APIs, implementing robust tests, and using profiling or telemetry tools
• Comfort implementing, resourcing, and debugging PySpark workflows
• Aptitude with data storage and caching, such as SQL and Redis
• Experience with production-ready machine learning packages such as scikit-learn or SparkMLExposure to some subset of the following concepts and technologies: queueing (ex., Kafka, Kinesis), data workflow managers (ex., Airflow, Luigi), cloud data warehouses (ex., Athena, BigQuery, RedShift)
• Required: Python, SQL, linux shell scripting
• Bonus Points: Go, Scala, Java