Senior Manager

Amazon

About the Role

The Amazon Business System (ABS) Integrations team builds systems integrations capabilities enabling business customers to buy on Amazon Business directly from their existing procurement, ERP, and finance systems. This involves developing APIs, protocols, and partner connections for fast, automated, and reliable procurement at global scale. The team designs, launches, and operates these integrations securely and seamlessly across diverse systems, regions, and customer needs. We are seeking a Senior Manager Applied Science to build and lead the science organization for Amazon Business Systems Integrations. This foundational leadership role spans the full integrations portfolio. The role will involve hiring, growing, and leading a team of applied scientists to define how we measure and improve the performance of AI agents and human-AI teams. A core responsibility is defining the science agenda, identifying high-impact problems and high-leverage opportunities. Potential research directions include Organizational Intelligence, AI Agent Experience/AiAX, and contextual behavioral security. The team will set scientific direction for how AI agents and humans perceive, reason, and act reliably within enterprise-scale computing environments.

Key Responsibilities

• Set the long-term scientific vision and team strategy: Define best-in-class agent performance evaluation and learning across Agentic WorkSpaces for computer-using agents and human-AI teams. Chart a multi-year research roadmap and build the team and plan to execute it. Secure buy-in from VP-level leadership.
• Hire and grow scientific talent: Own recruiting, calibration, development, and retention for the science team. Mentor scientists towards senior and principal scope and elevate scientific standards across the organization.
• Direct research on highly ambiguous novel problems: Guide the team through foundational challenges in agent perception, reasoning, evaluation, reliability, and human-AI collaboration, where neither the approach nor success criteria are pre-defined.
• Drive cross-organizational alignment: Collaborate with partner teams (AgentCore, Bedrock model teams, Identity, Security, MCP ecosystem) and across the Applied AI Solutions product portfolio with product and engineering leadership to ensure scientific decisions integrate into a coherent product.
• Deliver measurable business impact: Ensure the team's research translates to customer outcomes, including higher task accuracy, lower cost-per-action, faster time-to-production, measurable productivity for human-AI teams, and trust for scaling agent workflows.
• Establish scientific rigor and operational excellence: Set standards for experimentation, evaluation, and reproducibility, and establish mechanisms to maintain science organization productivity and accountability.
• Advance the state of the art: Enable and champion contributions to the external technical community through publications, patents, and open-source work to position AWS as a leader in the science of secure agent-computer interaction and human-AI teamwork.
• Translate science into shipped product: Partner with engineering, product, and program leaders to move models, evaluation, and learning systems from prototype to production services operating at massive scale without compromising reliability.
• Represent science in leadership and to customers: Serve as the scientific voice in organization-level planning and roadmap decisions across AWS and engage directly with enterprise customers on measuring and achieving agent performance, safety, and human-AI productivity.

Requirements & Tech Stack

• Master's degree in Computer Science, Machine Learning, AI, Statistics, or equivalent.
• Experience building large-scale machine learning and AI solutions at Internet scale.
• Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives.
• Experience hiring and leading experienced scientists, and developing junior members from academia or industry.
• Experience with big data technologies such as AWS, Hadoop, Spark, Pig, Hive.
• Experience with Java, C++, or other programming languages, and with R, MATLAB, Python, or an equivalent scripting language.

Qualifications

• 10+ years of practical work applying ML to solve complex problems for large-scale applications.
• 5+ years of hands-on work in big data, machine learning, and predictive modeling.
• 5+ years of people management experience.
• PhD in Computer Science (Machine Learning, AI, Statistics, or equivalent).
• Experience researching actual applications.