Senior Manager, Machine Learning Engineering

Facilitates the implementation of processes for machine learning (ML) model productionization to managers. Implements organizational standards around machine learning model readiness for deployment. Promotes organizational strategy around the automation of machine learning workflows. Promotes organizational strategy around trained model/system alignment with design criteria. Implements improvements to organizational processes for the identification and evaluation of potential data quality, security, and/or privacy issues and their impacts on modeling. Facilitates organizational troubleshooting and debugging support processes to address issues in machine learning infrastructure and workflow and create robust solutions. Alleviates the impact of obstacles to cross-functional collaboration efforts with multiple stakeholders to make, adopt and communicate technical decisions and shape the development and delivery of software. Implements organizational processes for the development, refinement, and maintenance of tools, platforms, environments, and services for internal use. Implements improvements to organizational processes for the development of efficient, bug-free code from scratch. Executes organizational strategy to maintain team awareness of current developments in the machine learning field and integration of this knowledge into model development. Key Responsibilities Machine Learning and Data Modeling – Model Productionization: – Facilitates the implementation of machine learning (ML) model productionization processes and process improvements. – Uses technical knowledge and business familiarity to empower the transformation of machine learning prototypes into production-ready models. – Implements strategy to build technical expertise and readiness across team related to model productionization. – Alleviates the impact of obstacles on collaboration 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: – Implements multiple team standards around ML model readiness for deployment (e.g., model scaling, model code cleaning, and meeting production quality standards). – Promotes multiple team strategy around the automation of 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: – Promotes multiple team strategies around trained model/system alignment with design criteria. – Identifies improvements within multiple team processes around deployed model performance evaluation and troubleshooting. – Facilitates the creation of novel metrics that provide analytical insights to non-technical stakeholders into how well machine learning models are operating. Model Development and Deployment – Data Quality: – Implements improvements to multiple team processes for the identification and evaluation of potential issues related to data quality (e.g., bias, fairness), data security, and data privacy, and the minimization of their impacts on data analyses and modeling. – Promotes multiple team strategies for preparing for and enabling model training. Internal Collaborations and Impacts – Model Integration and Operation: – Implements improvements to multiple team strategy that forms partnerships for collaboration with multiple stakeholders (e.g., data scientists, software developers) to integrate ML models into new or existing systems. – Maintains accountability of model development and operations teams in the smooth deployment and continuous improvement of ML models. – Builds the team's knowledge of operational considerations of model deployment (e.g., performance, scalability, stability, maintenance) to facilitate multiple team processes. – Facilitates expert troubleshooting and debugging support efforts to address issues in machine learning infrastructure and workflow and create robust solutions to prevent future problems. Internal Collaborations and Impacts – Tool Development: – Implements process improvements for the development, refinement, and maintenance of tools, platforms, environments, and services for internal use. Internal Collaborations and Impacts – Coding and Documentation: – Implements improvements to multiple team processes for the development of efficient, bug-free code from scratch, as well as the maintenance and organization of the existing codebase. – Maintains team adherence to best practices for version control, code review, and code delivery/deployment. – Monitors professional documentation for technical processes (experimentation, data collection and analyses, model building). Machine Learning Expertise: – Implements multiple team strategies to maintain team awareness of current developments in the machine learning field and integration of this knowledge into model development. – Utilizes familiarity with the usage and development of third-party machine learning frameworks, packages, and libraries (e.g., PyTorch, TensorFlow, Keras) to identify improvements to multiple team processes around their performance and scalability, and integrate them into production environments. Core Responsibilities Planning & Execution: – Manages multiple medium- to large-scale projects or initiatives across teams, ensuring timelines, deliverables, and budgets when applicable are monitored and met. – Provides direction to teams on project work, setting priorities, and aligning with business needs. – Guides teams on adjusting plans to accommodate resource or timeline changes. Collaboration & Partnership: – Drives cross-functional partnerships to align expectations and shared objectives across multiple teams. – Coaches team members to develop strategic relationships with business leaders, stakeholders, and external partners to foster collaboration and long-term success. – Promotes inclusivity by actively seeking and listening to diverse perspectives, ensuring others feel heard and respected. Problem Solving: – Provides direction to multiple teams on addressing complex operational and/or technical issues as well as providing guidance on analyzing complex data and/or information to identify solutions. – Reviews and provides insights into unresolved or critical issues, helping the team to identify potential solutions. Continuous Learning: – Models engaging in continuous learning to deepen expertise and stay ahead of industry trends, integrating best practices into strategic planning. – Leverages feedback to drive personal and team skill improvements. – Identifies skill gaps across teams, and empowers team members to pursue learning and knowledge sharing opportunities that build their expertise in new areas and coaches them to apply learnings to advance the organization. Continuous Improvement: – Drives team to collaborate on, develop, and implement ideas to increase the efficiency and effectiveness of processes, protocols, and workflows within and across teams, providing oversight. – Guides team to adopt new ideas for alternative approaches and methods and encourages feedback for continued improvement. Performance and Development: – Drives performance across teams by providing feedback and coaching in alignment with performance management processes, guidelines, and expectations. – Discusses development goals with team members, shares opportunities to facilitate career development, and ensures individual goals are aligned with broader organizational goals. – Develops and manages talent acquisition pipeline by leading candidate interviews, monitoring promotion eligibility, and/or orchestrating talent resources. Career Level - M3