Principal Machine Learning Engineer - Full-time

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** Implements machine learning (ML) models for production. Ensures the readiness of machine learning models for deployment in production. Automates machine learning workflows. Creates infrastructure and frameworks to monitor the performance of machine learning models in deployment. Evaluates potential data quality, security, and/or privacy issues and their impacts on modeling. Provides troubleshooting and debugging support. Addresses issues in machine learning infrastructure and workflows. Collaborates with stakeholders to integrate machine learning models into new or extant systems. Develops, maintains, and refines tools, platforms, and services for internal use. Develops efficient, bug-free code from scratch. Maintains familiarity with current developments in the machine learning field and integrates knowledge into model development. • *

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** • *MachineLearning and Data Modeling – Model Productionization:** – Utilizes machine learning (ML) and software development knowledge to implement ML models for production. – Engages in transforming machine learning prototypes into production-ready models. – Collaborates with multiple stakeholders, such as Development Leads, Product Management, Operations, and Release Management, to make, adopt, and communicate technical decisions, and shape the development and delivery of software. • *ModelDevelopment and Deployment – Model Deployment:** – Ensures ML model readiness for deployment by scaling models, cleaning model code, and ensuring production quality standards are met. – Automates machine learning workflows, from data extraction, transformation, and loading (ETL) to model deployment and monitoring, to establish the continuous integration and continuous delivery of machine learning solutions. • *ModelDevelopment and Deployment – Model Performance:** – Creates infrastructure and frameworks to monitor the performance and alignment with design criteria of trained models and/or systems. – Proactively monitors the performance of deployed models and troubleshoots independently or in collaboration with Data Science. – Develops novel metrics that provide analytical insights to non-technical stakeholders on how well machine learning models are operating. • *ModelDevelopment and Deployment – Data Quality:** – Evaluates potential issues related to data quality (e.g., bias, fairness), data security, and data privacy, and minimizes their impacts on data analyses and modeling. – Engages in tasks such as data cleaning, preprocessing, and feature identification to prepare for and enable model training. • *InternalCollaborations and Impacts – Model Integration and Operation:** – Collaborates with multiple stakeholders (e.g., data scientists, software developers) to integrate ML models into new or existing systems. – Maintains the partnership between model development and operations, ensuring smooth deployment and continuous improvement of ML models. – Understands operational considerations of model deployment (e.g., performance, scalability, stability, maintenance). – Provides expert troubleshooting and debugging support, addresses issues in machine learning infrastructure and workflow, and creates robust solutions to prevent future problems. • *InternalCollaborations and Impacts – Tool Development:** – Develops, maintains, and refines tools, platforms, environments, and services for internal use. • *InternalCollaborations and Impacts – Coding and Documentation:** – Develops efficient, bug-free, medium-complexity code from scratch, and properly maintains and organizes the existing codebase. – Implements best practices for version control, code review, and code delivery/deployment. – Builds and maintains professional documentation for technical processes (experimentation, data collection and analyses, model building). – Tests and reviews code for bugs. • *MachineLearning Expertise:** – Maintains familiarity with current developments in the machine learning field and integrates knowledge into model development. – Maintains familiarity with the usage and development of third-party machine learning frameworks, packages, and libraries (e.g., PyTorch, TensorFlow, Keras) to continuously evaluate their performance and scalability, and integrate them into production environments. • *Core