Software Dev Engineer, Sales AI
Amazon
- Location
- CA, ON, Toronto
- Employment
- Full Time
- Work model
- On-Site
- Level
- Mid
- Posted
- 19h ago
Skills
About this role
Are you interested in shaping the future of Advertising and B2B Sales? Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, delivering billions of ad impressions and generating billions in revenue. We are looking for a Software Development Engineer to join our team and help build the end-to-end recommendation generation and delivery platform — powered by the Advertiser Intelligence Center (AIC) and upstream contextual signals. You will design and implement the systems that aggregate advertiser context, generate actionable recommendations, and deliver them to sales users and advertisers across multiple surfaces (Global Action Center, Pending Changes, Advertiser Console). Your work will span the full lifecycle — from ingesting contextual signals (advertiser performance data, campaign patterns, audience insights), to powering AI agents that generate recommendations, to building the delivery infrastructure that serves those recommendations at scale through our Tactical Recommendations Service (TRS). This is an opportunity to work across the entire recommendation stack: context aggregation, AI-driven recommendation generation, and production delivery systems that serve thousands of account team members and millions of advertisers globally. You will work at the intersection of large-scale data systems, Generative AI, and production agent frameworks to build systems that are fast, reliable, and intelligent enough to power autonomous sales workflows. Why You Will Love This Opportunity - End-to-end ownership: Own the full recommendation lifecycle — from upstream contextual signal ingestion through AI-powered generation to multi-surface delivery and adoption measurement. - Impact at scale: Your work powers AI agents and recommendation surfaces used by thousands of account team members serving Amazon's largest advertisers globally - AI-native development: Work hands-on with agent frameworks, retrieval-augmented generation pipelines, and LLM-powered systems in production. - Entrepreneurial team:We move fast, experiment often, and ship real products. Small team, big mandate. - Career growth: Amazon Advertising is one of the fastest growing businesses at Amazon, with high visibility to senior leadership. Key job responsibilities • Collaborate with experienced cross-disciplinary Amazonians to conceive, design, and bring innovative products and services to market. • Design and build innovative technologies in a large distributed computing environment and help lead fundamental changes in the industry. • Create solutions to run predictions on distributed systems with exposure to innovative technologies at incredible scale and speed. • Build distributed storage, index, and query systems that are scalable, fault-tolerant, low cost, and easy to manage/use. • Design and code the right solutions starting with broadly defined problems. • Work in an agile environment to deliver high-quality software. A day in the life - Design and build scalable services that identify, store and serve tactical recommendations to internal Sales users and external Advertisers. - Build and optimize data pipelines that aggregate opportunity signals from upstream ML models and produce actionable recommendations. - Build and iterate on LLM-powered agents that ingests advertiser context (account history, performance data, deal signals, behavioral patterns), reasons over it, and generates tailored recommendations for end users. - Partner with Applied Scientists on recommendation scoring, impact estimation, and segmentation models. - Drive technical decisions on data modeling, storage strategies, and system architecture for end-to-end opportunity Identification pipeline to recommendation delivery.
About the team
Within the Advertising Sales organization, Sales AI is building the intelligent systems that transform how account teams operate — from actionable insights and recommendations to Generative AI-powered agents that