Principal Machine Learning Engineer

Key Responsibilities

Machine Learning 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.

Model Development 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.

Model Development 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.

Model Development 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.

Internal Collaborations 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.

Internal Collaborations and Impacts – Tool Development:

–         Develops, maintains, and refines tools, platforms, environments, and services for internal use.

Internal Collaborations 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, c