ML-Powered Ads Infrastructure Engineer

Socket.dev; New York (NY); On-site; USD 158,000 - 214,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

RSU program

Job summary

Amazon Ads is seeking engineers to build scalable, low-latency distributed systems powering NLP, personalization, and ML-driven ad experiences for hundreds of millions of shoppers worldwide.

You will partner with scientists to deploy production ML models, design data pipelines, and create robust APIs. This team values responsible AI, fast iteration, and strong collaboration across product, science, and engineering.

Qualifications

• 3+ years of non-internship professional software development experience.
• 2+ years of non-internship design or architecture (design patterns, reliability and scaling) experience.
• Experience programming with at least one software programming language.

Responsibilities

• Build large-scale, low-latency distributed systems that power NLP, personalization, and ML-driven ad experiences.
• Partner with the science team to design, build, test, and deploy production systems serving machine learning models at scale.
• Build ML infrastructure and data processing pipelines that support new advertising experiences globally.
• Design, build, and operate key pieces of customer-facing functionality for hundreds of millions of shoppers.
• Translate business and scientific requirements into robust APIs and maintainable software solutions.
• Instrument systems to measure shopper and advertiser response to changes, driving data-informed iteration.
• Drive major product and engineering initiatives that help merchants, retailers, and brand owners grow incremental sales through native advertising.

Education

Bachelor's degree in computer science or equivalent

Job description

Amazon Ads is seeking engineers to build scalable, low-latency distributed systems powering NLP, personalization, and ML-driven ad experiences for hundreds of millions of shoppers worldwide.

You will partner with scientists to deploy production ML models, design data pipelines, and create robust APIs. This team values responsible AI, fast iteration, and strong collaboration across product, science, and engineering.