Principal Machine Learning Engineer

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.

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, 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. Machine Learning 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 Responsibilities Planning & Execution: – Manages and coordinates moderately complex tasks, monitoring timelines and deliverables to ensure timely completion and adherence to requirements for a moderately sized project or initiative. – Efficiently delegates, monitors, and prioritizes work across multiple projects, providing technical oversight and adjusting plans to address shifts in resources or timelines. Collaboration & Partnership: – Collaborates across the organization to align on expectations and achieve shared objectives. – Leverages understanding of business leaders, stakeholders, and/or customers to ensure proposed solutions meet their needs. – Supports inclusivity by actively seeking and listening to diverse perspectives, ensuring others feel heard and respected. Problem Solving: – Identifies and addresses moderately complex issues by analyzing a wide range of data and/or information to identify solutions in accordance with standard practices. – Proactively escalates unresolved or critical issues with a thorough assessment and suggests potential solutions. – Reviews, contributes to, and documents problem solving strategies. Continuous Learning: – Pursues learning opportunities to expand knowledge and skills and/or tools in new areas and stays abreast of the latest industry trends and best practices. – Proactively seeks and leverages ongoing feedback and training to improve skills. – Coaches and mentors junior team members, fostering continuous learning and knowledge sharing within and across teams. Continuous Improvement: – Develops ideas, recommends updates, and/or collaborates on the implementation of process improvements to increase the efficiency and effectiveness of processes, protocols, and workflows across teams, and evaluates the impact on key stakeholders. – Solicits feedback from others on ideas for alternative approaches and methods for continued improvement. Performance and Development: – Contributes to the talent development pipeline by participating in candidate interviews, assessing candidates, and providing hiring recommendations.