Applied Science Manager, Data Center Core Network Engineering

AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we're the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we're looking for talented people who want to help. You'll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You'll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you'll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. The Core Networking organization is seeking an experienced Applied Science Manager to lead a team of Scientists, Software Engineers, and Data Engineers in revolutionizing network operations through intelligent automation. In this role, you will define and execute multi-year science strategies to address complex challenges in Network Design, performance scaling, and Operations. You will lead the production strategy and execution for a diverse range of models (statistical models, GenAI, autonomous agents) in a fast-paced operational environment. This leadership role requires owning machine learning operations and deployment frameworks while building scalable self-service analytics and science infrastructure that enables data driven culture in Core Networking and beyond. Key job responsibilities Team Leadership & Development
- Lead and mentor a cross-functional team of applied scientists, data scientists, software engineers, and data engineers
- Hire top-tier talent and provide ongoing coaching for career growth and technical development
- Foster a collaborative, innovative environment aligned with organizational objectives
- Methodically expand the team's domain expertise across diverse science areas Self-Healing Network Development
- Collaborate with all stakeholder Design and implement autonomous network remediation systems that can take corrective actions with mínimal human intervention
- Develop intelligent agents and ML models that collaborate with human network domain experts to identify and resolve network issues
- Build predictive analytics solutions to proactively detect performance issues and anomalies before they impact operations
- Create automated decision-making frameworks that balance autonomous actions with human oversight Research & Technical Strategy
- Conduct research in GenAI, autonomous agents, LLMs, machine learning, and applied statistics for network operations
- Set technical and scientific direction by defining vision, roadmap, and success metrics for high-impact ML and AI projects
- Stay current with latest developments in network automation and AI-driven operations
- Own machine learning operations and deployment frameworks for production environments
- Support instrumentation and monitoring of production models through A/B test design and validation methods
- Manage suite of production science solutions with infrastructure as code practices
- Work in ambiguous environments with opportunity to influence and contribute to organizational strategy
- Contribute to complex High-Level-Designs spanning multiple fabric engineering teams
- Provide technical feedback on Low-Level-Designs with deep networking expertise
- Collaborate with stakeholders to define project goals, success criteria, and deliverables
- Communicate complex technical concepts clearly to both technical and business audiences Project Management & Execution
- Oversee end-to-end lifecycle of data science projects from problem definition to model deployment
- Drive execution through sprint, quarterly, and annual planning with clear goal-setting and stakeholder alignment
- Balance delivering immediate results for operations customers while maintaining focus on long-term science roadmaps
- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations
- Experience communicating with users, other technical teams, and management to collect requirements, describe software product features, and technical designs
- Network domain expertise with understanding of computer and cloud networking challenges across data center network fabrics
- Experience with autonomous systems and intelligent agents for infrastructure ma