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Utilize tools for continuous integration and continuous deployment (CI/CD), and Infrastructure as Code (IaC) like Terraform to automate and improve development and release processes. This role involves working in Java, and working on Machine Learning pipelines for data collection or batch inference.
Demonstrated experience using machine learning, deep learning, statistical methodology, and simulation/optimization modeling in geospatial, network topography, recommendation-systems, environmental systems and/or agronomic problems. Extract, load and transform data (ETL) from structured and unstruct
Contribute analysis and perspective that inform portfolio-level decisions, including explaining model behavior, tradeoffs, and uncertainty to senior technical and business leaders. Design and run experiments to evaluate model performance, measure impact on approval rates and loss, and inform credit
You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems. Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses. We’re hiring a
The Learning Center also works with a diverse portfolio of funders and partners - including Managed Care Plans, purchasers such as Covered California, and foundations including the California Healthcare Foundation and Blue Shield Foundation of California- to build Population Health Management (PHM)
The team develops and enhances various AI models, ML services and tools including LLM fine-tuning, alignment and optimization, RAG/Search, LLM evaluation and testing automation, feedback-based learning and guardrail for a wide range of applications in Airbnb. As a principal machine learning engine
The compensation offered for this role will be based on multiple factors such as location, the role’s scope and complexity, and the candidate’s experience and expertise, market data and may vary from the range provided. You will design and ship predictive models and data products, improve the qu
Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impac