Machine Learning Engineer

Machine Learning Engineer at Amazon Ads based in New York, NY (onsite), responsible for building and deploying near-real-time ML systems for ad relevance, spanning data ingestion pipelines, feature generation, and real-time inference at scale.

Responsibilities

• Design, build, and operate near-real-time data ingestion pipelines using Apache Flink, Kinesis, and DynamoDB to process shopper behavioral signals at scale (100K+ TPS, sub-second latency).
• Develop and maintain ML feature generation services that transform raw customer interactions into ML-ready signals consumed by prediction models across Amazon Ads.
• Enhance the scalability, automation, and efficiency of large-scale training and real-time inference systems.
• Build and improve data quality monitoring frameworks, automated alerting, and self-healing mechanisms to ensure signal reliability at 99.9%+ availability.
• Collaborate with applied scientists and partner engineering teams to onboard new shopper signals, define feature schemas, and optimize serving latency for real-time ad personalization.
• Contribute to system design discussions, propose technical solutions for ambiguous problems, and drive implementation end-to-end with guidance from senior engineers.

Requirements

• 2+ years of non-internship professional software development experience.
• Experience programming with at least one software programming language.
• 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience.
• Experience in machine learning, data mining, information retrieval, statistics or natural language processing.

Technologies

• Apache Flink
• Kinesis
• DynamoDB

Benefits

• Sign-on payments
• Restricted stock units (RSUs)
• 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
• Parental leave

A Day in the Life

• Highly analytical: you solve problems backed by verifiable data, driving processes, tools, and statistical methods that support rational decision-making.
• Humbitious: ambitious yet humble; you use introspection and feedback to continually raise the bar.
• Engaged by ambiguity: you explore new problem spaces with unique constraints and non-obvious solutions, quickly identifying gaps and the right people to fill them.