Software Development Manager, Autonomous Agentic Bidding
Amazon
Description
We are seeking an exceptional Software Development Manager to lead the Autonomous Agentic Bidding team within the Autonomous Sponsored Products and Brands (SPB) organization in Amazon Ads. This is a high-impact leadership role at the intersection of artificial intelligence, large-scale distributed systems, and advertising technology, where you will drive the development of intelligent, hands-off controls that revolutionize how advertisers achieve their business objectives.
In this position, you will lead a team building sophisticated autonomous bidding systems that process billions of advertising transactions daily across Amazon's tier-1 infrastructure. Your team's work directly impacts advertiser success by leveraging highly advanced techniques including predictive modeling, generative AI, control theory, agent-based reinforcement learning, and advanced optimization methods to automatically adjust bids in real-time without advertiser intervention. This autonomous approach enables advertisers to focus on strategy while our systems handle the complex, millisecond-by-millisecond bidding decisions that maximize their return on investment.
The technical challenges are substantial: you'll be architecting systems that must maintain low latency while processing massive scale, incorporating multiple ML models that learn and adapt continuously, and ensuring rock-solid reliability for a mission-critical advertising platform that generates significant revenue for Amazon and its advertisers. Your solutions must balance competing objectives—advertiser goals, marketplace dynamics, customer experience, and system constraints—all while operating autonomously with appropriate guardrails and explainability.
Beyond the technical complexity, this role offers the opportunity to shape the future of advertising at Amazon. As autonomous systems become increasingly sophisticated, your team's innovations will define how advertisers interact with Amazon Ads, potentially reducing manual campaign management from hours per week to minutes per month. You'll be building the foundation for truly autonomous advertising—systems that understand advertiser intent, adapt to market conditions, and deliver measurable business outcomes without constant human oversight.
The ideal candidate brings deep expertise in both machine learning systems and large-scale distributed computing, combined with proven leadership in building and scaling high-performing engineering teams. You should be equally comfortable discussing reinforcement learning algorithms with applied scientists, system architecture with senior engineers, and business impact with product leaders. This role requires someone who can balance innovation with operational excellence, move fast while maintaining quality, and inspire teams to solve problems that have never been solved before.
Key job responsibilities
- Define long term vision and product strategy for bidding systems for Sponsored Products and Sponsored Brands
- Lead the end-to-end architecture, design, and implementation of autonomous bidding systems processing billions of advertising transactions daily with high availability, low latency, and strict SLA requirements
- Drive technical decisions around predictive modeling approaches, generative AI integration, multi-agent reinforcement learning algorithms, control theory applications, and real-time optimization methods for autonomous bid adjustment
- Ensure system scalability, reliability, and performance for tier-1 production services handling mission-critical advertising workloads across multiple AWS regions
- Establish engineering best practices for ML operations (MLOps), including model training pipelines, A/B testing frameworks, continuous deployment, monitoring, and automated rollback mechanisms
- Design fault-tolerant systems with appropriate circuit breakers, fallback strategies, and graceful degradation to maintain advertiser trust during system anomalies
- Balance technical debt with feature velocity, making strategic decisions about when to refactor versus when to iterate
- Build, mentor, and grow a high-performing team of software engineers and and applied scientists working on advanced autonomous systems and agentic AI