Senior Technical Program Manager

Role overview Build the operating system for product and engineering delivery at a growing software or AI organization. As a hands-on individual contributor, you will coordinate Applied AI, Engineering, Research, and field engineering teams, making plans, dependencies, and delivery status clear without relying on direct management authority. The work spans development processes, technical alignment, launch readiness, and capacity planning.

Responsibilities - Consolidate delivery tools and establish a reliable source of truth for the software development lifecycle. - Run the end-to-end delivery process, set and maintain deadlines, and provide a six-month view of team capacity. - Design automated development workflows that incorporate security, privacy, and compliance reviews. - Define the handoff for field-built extensions, including when work should enter the core product, be productized, or be retired. - Coordinate cross-team dependencies across time zones and facilitate early agreement on APIs, terminology, and system design. - Lead regular delivery reviews, surface risks and decisions for leadership, and help completed capabilities reach launch.

Requirements - At least 7 years in technical program management, engineering management, or a blended role at a fast-moving software or AI company. - Direct familiarity with machine learning or large language model development lifecycles, sufficient to assess estimates and delivery plans. - Experience establishing development processes and tools in an organization of roughly 50–300 people. - Ability to influence senior engineers and researchers without managing them, and to work effectively amid ambiguity. - Experience coordinating engineering across multiple time zones and a strong bias toward shipped outcomes. - Clear written communication for documentation and asynchronous coordination.

Nice to have - Enterprise B2B software experience, particularly with both a platform product and field or solutions engineering. - Experience turning customer-specific technical work into product capabilities. - Familiarity with production model deployment, inference infrastructure, or MLOps.