From Amazon's posting. “We” and “our” refer to the employer.
Build the AI-powered bidding controls and recommendation systems that help millions of advertisers optimize their campaigns on Amazon — developing agentic architectures, model fine-tuning infrastructure, and experimentation platforms that directly impact how advertisers create, grow, and manage their business. Your technical decisions will translate directly into advertiser success and the products customers discover on Amazon.
The Advertiser Controls team within Sponsored Products and Brands is building three critical platform capabilities. First, AI engineering infrastructure that powers our bidding and recommendation systems — systems for model fine-tuning, reinforcement learning, model inferencing, and preference optimization, with evaluation frameworks that ensure safety, reliability, and trust at scale.
Second, agentic architectures that allow AI systems to work together seamlessly — agent-to-agent communication protocols for discovery, negotiation, and task handoff, lifecycle management for agent sessions, and state management that persists context across multi-step workflows. Third, experimentation systems that support multiple concurrent online experiments, accelerating the team's ability to test new bidding strategies and learn from real advertiser behavior.
You'll work backwards from advertiser needs — collaborating with product managers, scientists, and engineers to understand business problems and translate them into technical solutions, seeking guidance from senior engineers and principals on complex architectural tradeoffs.
Key job responsibilities
Design and develop agentic platform components using GenAI/ML technologies to deliver low-latency, secure advertiser experiences
Build scalable systems that process millions of data points and optimize for cost efficiency through resource utilization, token consumption, and memory management
Develop conversational AI and natural language interactions for advertiser bidding guidance
Collaborate with cross-functional teams to integrate AI-driven solutions across the advertising ecosystem
Identify and eliminate root causes of operational issues with permanent fixes; proactively improve team operations, tooling, and processes
Write high-quality, maintainable code with clear documentation
Provide meaningful code reviews, mentor engineers, and train new teammates on system architecture and operations
About the team
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights.
We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising.
Basic Qualifications
3+ years of non-internship professional software development experience
2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
1+ years of software development engineer or related occupational experience
1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
Experience programming with at least one software programming language
Strong technical fluency in Generative AI, including a deep understanding of large language models (LLMs), model fine tuning, prompt engineering, Reinforcement Learning from Human Feedback (RLHF), Retrieval-Augmented Generation (RAG), AI model trade-offs (e.g., model size, latency, cost, and output quality).
Preferred Qualifications
3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
Bachelor's degree or equivalent
Experience developing, deploying and managing AI products at scale
Equal Opportunity
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location.
Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, NY, New York - 158,100.00 - 213,800.00 USD annually
USA, WA, SEATTLE - 143,700.00 - 194,400.00 USD annually
About this listing
LAYIQ is an independent job-discovery service. This listing does not imply a partnership with or endorsement by the employer. Review the original posting for current details and availability.
