We are seeking a Senior Staff Technical Program Manager to lead the strategy, execution, and operational excellence of a mission-critical initiative developing large-scale geospatial datasets for next-generation AI and machine learning systems. 

This is a highly visible technical leadership role responsible for driving cross-organizational programs that span machine learning, Geographic Information Systems (GIS), data engineering, technical art, infrastructure, and external annotation partners. You will establish the technical and operational framework for dataset development, define scalable processes, influence engineering and product roadmaps, and ensure the delivery of high-quality training data that accelerates AI innovation. 

As a Senior Staff TPM, you will operate as a force multiplier across multiple teams, aligning senior stakeholders, identifying systemic risks before they materialize, and driving long-term improvements in tooling, quality, governance, and execution. 

Key Responsibilities 

• Strategic Program Leadership: Lead the end-to-end strategy, planning, execution, and delivery of large-scale AI dataset development programs, establishing roadmaps, milestones, success metrics, and risk mitigation plans. 
• Cross-Functional Leadership: Drive alignment across ML engineers, GIS engineers, technical artists, product stakeholders, infrastructure teams, and external annotation partners to deliver complex technical initiatives. 
• Technical & Organizational Influence: Partner with engineering and research leaders to define scalable data pipelines, annotation workflows, and operational best practices that improve quality, efficiency, and model performance. 
• Vendor Strategy & Operations: Own strategic vendor relationships, including onboarding, quality assurance, throughput planning, operational metrics, and continuous process improvement. 
• Data Quality & Governance: Define annotation standards, quality metrics, validation processes, and data governance practices to ensure reproducible, high-quality datasets across the organization. 
• Tooling & Infrastructure: Partner with engineering teams to improve annotation platforms, automation, and data infrastructure, ensuring scalable, secure, and reliable dataset production. 
• Program Excellence: Identify cross-program risks, manage dependencies, establish operational metrics, and communicate program health and strategic recommendations to senior leadership. 
• Compliance & Ethics: Ensure data collection, annotation, and governance practices adhere to privacy, security, regulatory, and responsible AI standards. 
• Documentation & Knowledge Management: Drive documentation standards that ensure data provenance, technical decisions, operational processes, and institutional knowledge are maintained and easily transferable.