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, 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.