Senior TPM - ML Science, Deep Science for Systems and Services
Amazon
DESCRIPTION
We are seeking an experienced Senior Technical Program Manager to define and deliver high-impact programs with broad cross-organizational, cross-business, and significant technology impact within DS3. This role requires exceptional technical program management skills, strategic thinking, and the ability to operate with complete independence in ambiguous environments.
As a Senior Technical Program Manager in DS3, you will be at the forefront of AI/ML innovation at AWS, driving initiatives that shape the future of machine learning systems and services.
Key job responsibilities
* Strategic Program Leadership: Define and deliver important programs with broad cross-organizational, cross-business, or significant technology impact in the AI/ML space.
* Investment Planning & Prioritization: Help formulate new investment ideas and lead the working backwards process to develop concepts that meet AWS standards for investment.
* Strategy & Goal Management: Own high-level strategy development and goal tracking processes across key cross-organizational initiatives.
* Technical Leadership: Bring strong, data-driven, and strategic technical judgment to decisions. Maintain deep knowledge of core system technologies related to AWS AI/ML research. Moderate and guide technical discussions to successful decisions.
* Cross-Functional Alignment: Proactively drive business and system-level reviews to align teams across organizations. Identify gaps and opportunities in architectures and processes.
* Solution Simplification: Decompose complex problems into straightforward solutions that minimize redundant engineering/science effort across teams.
* Leadership Communication: Partner effectively with all levels of management. Clearly represent complex decisions, trade-offs, and solutions to senior leadership.
About the team
Deep Science for Systems and Services (DS3) is a science organization within AWS Compute & ML Services focused on advancing AI/ML technologies in several key areas including large model inference cost performance, large model inference operational trust and safety, model customization, multimodal science with a focus on media/document content extraction, fault resilient training, classical machine learning (ML) like Auto Machine Learning and Graph Machine Learning.