Principal Machine Learning Engineer

– 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.

– 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.

– 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.

– 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.

– 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.

– 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.

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