Director, Software Development, Workforce Intelligence and Skilling

Amazon"s People Experience and Technology (PXT) organization is seeking a Director of Software Development to lead Workforce Intelligence and Skilling - the autonomous workforce intelligence platform in Amazon combining enterprise listening, adaptive learning, behavioral skills measurement, causal evaluation, and agentic intervention at 1.5 million+ employee scale. This leader will own the technical strategy, platform architecture, and engineering execution across five product pillars: Continuous Listening, Skills Intelligence and Workforce Development, Autonomous Learning Operations, Measurement Science and Causal Attribution, and Closed-Loop Intelligence and Autonomous Action.

The successful candidate will scale this platform from a set of connected systems into a fully autonomous closed-loop intelligence engine: one where listening signal drives intervention, intervention drives development, development drives measured outcomes, and outcomes compound intelligence. Every capability ships as a headless agentic primitive - composable, API-first, and multi-tenant-ready - so intelligence finds leaders at the moment of decision rather than waiting to be found.

Key job responsibilities

• Lead Platform and Technical Strategy: Own the technical roadmap for Workforce Intelligence and Skilling, driving the architecture from five connected pillars into a fully autonomous closed-loop intelligence platform serving 1.5 million+ employees across 37 languages.
• Architect Agentic Primitives at Scale: Oversee architectural decisions for headless, composable agentic services - Autonomous Detection, Autonomous Intervention, Signal Fusion, Content Creation, Personalization, and Learning Effectiveness - built API-first and multi-tenant-ready on AI interfaces.
• Scale Autonomous Learning Operations: Drive the transition of Amazon Learn from human-operated administration to system-operated, targeting a 50% reduction in the 24,947 annual admin support tickets and elimination of the 6.14-hour monthly per-associate training burden through autonomous governance agents.
• Drive Causal Measurement Science: Build and scale Amazon Causal Evaluation (ACE), Multi-Level Causal Chain Attribution, and the Intervention Effectiveness Library - delivering the proof infrastructure that answers which programs improve retention, which training changes behavior, and which career pathways produce the best outcomes.
• Build Skills Intelligence and Career Development: Scale the Learn Agent and Career Intelligence experience - fusing Employee 360 context, live labor-market data, and Amazon"s funded catalog (Career Choice, AWS Skill Builder, Machine Learning University (MLU), and free AI certifications) - into a single front door across 35+ upskilling programs
• Lead Global Engineering Teams: Direct engineering teams across global locations, fostering rapid experimentation and a culture of autonomous, outcomes-driven delivery.
• Drive Business Impact Through KPIs: Establish and own the platform"s definitive scorecard - autonomous interventions per quarter, career transition outcomes, causal return on investment (ROI) per quarter, listening reach, and leader action rate - demonstrating impact on productivity, retention, and workforce readiness.
• Position for Externalization: Architect every primitive to serve any enterprise tomorrow without re-architecture, advancing Amazon"s opportunity to externalize the world"s only at-scale autonomous workforce intelligence platform through Future Ready 2030.

A day in the life

You will lead strategic discussions with PXT leadership on the platform"s autonomous intelligence roadmap while driving rapid experimentation across your engineering teams. Your day spans deep technical architecture reviews on multi-agent orchestration and causal science infrastructure to critical business metrics conversations with senior leaders. You will review agentic workflow designs for autonomous pruning agents, partner with AWS Bedrock and Amazon Q teams on intelligence delivery primitives, and engage with science teams on ACE evaluation frameworks. You will balance shipping headless primitives that deliver immediate value independently against assembling them into closed-loop intelligence cycles that compound over time. You will define the metrics that prove the platform is working - not just adoption counts, but career transitions, training burden reductions, and causal ROI - and hold the organization accountable to them.