Software Development Manager, Amazon Optics, Amazon Optics

Overview In this role you lead engineers and applied scientists to build ML-driven analytics and automation for security operations. You own end-to-end ML systems, from data pipelines to production serving, impacting security decisions across AWS and Amazon locations. You collaborate with cross-functional teams and customers to translate needs into scalable, production-grade solutions. You will shape the analytics platform’s strategy and influence security at scale, while fostering a culture of ownership and growth. Compensation / Benefitsflexible work hoursDEI learning experienceshealth insurance (medical, dental, vision)RSUs (restricted stock units)401(k) matchingpaid time off ResponsibilitiesLead a team of engineers and applied scientists building ML-driven analytics and automation services for physical security operationsOwn the full lifecycle of ML systems: data pipelines, model training, production serving, and continuous evaluationPartner with security operations customers to identify automation opportunities and translate them into scalable solutionsDrive technical strategy for real-time analytics services processing security signalsIndependently own software architecture or product suite with significant business impactDefine customer-centric vision and ensure the product resolves their needsMitigate risks proactively and maintain engineering excellence through reviews and best practicesImprove engineering practices across API, component, feature, and UI levelsIdentify and develop new workflows and delegate work while maintaining ownership Key requirements3+ years of engineering team management experience7+ years of experience working within engineering teams3+ years designing or architecting systems (patterns, reliability, scaling)3+ years building ML models for business applicationsKnowledge of full software/hardware/networks development lifecycle including coding standards, code reviews, source control, build, testing, and live-site operationsExperience partnering with product or program management teamsExperience with ML model architectures, training strategies, evaluation methodologies, and production serving trade-offs from direct involvementproactiveresponsiveflexibleMachine Learning and production ML systemsdata pipelines and model trainingproduction serving and continuous evaluation