New York, NY, US
5 days ago
Software Dev Engineer II, Prime Video Data Platform Team
Do you want to take on one of the most important engineering challenges to shape the future of video streaming? Join us to define the next generation of how and what Amazon customers will be watching!

Prime Video (PV) is a premium streaming service that offers customers the greatest choices in what to watch, and how to watch it. PV's mission is to become the global entertainment destination for customers to enjoy movies, TV shows and live events streamed instantly to all of their devices including TVs, tablets, game consoles and PCs worldwide. We are a young and evolving business within Amazon where creativity and drive can have a lasting impact on the way video is enjoyed worldwide. You will be encouraged to see the big picture, be creative, and positively impact millions of customers. We’re building the future of streaming — yes, it’s challenging, but it’s also a lot of fun.

PVPD’s Data Platform will be Prime Video’s central hub for all data required for personalization, including impressions, streams, purchases, and other customer interactions helping millions of customers find their next video. We are looking for a Software Development Engineer with a strong technical background, experience with data processing, building and maintaining large-scale, high-traffic systems with a love for both operations and innovation. As part of our team you'll build fundamental infrastructure building blocks to power personalization experiences on Prime Video storefronts while supporting a service that handles billions of requests per day.

Key job responsibilities
You will work with a team of talented data and software engineers and cross-functional partners including Product and Science to deliver innovations to our global customers to build a long-term relationship with Prime Video by bringing them back to the service, curates and personalizes their storefront to highlight the diversity and depth of the PV catalog.

You will build solutions that leverage the latest technologies including large language models and other machine learning techniques. You will work with (1) high volumes of data (2) use known models or optimized models (3) set up ML training infrastructure and (4) test and validate your changes in production worldwide.

A successful candidate will have strong technical skills, great analytical reasoning ability, excellent communication skills, high creativity, and motivation to achieve results in a fast-paced environment. You should also have industry experience in building scalable systems, working with large data sets and ML models, understanding their limitations and best practices. You feel comfortable adapting to evolving requirements based on learnings. Lastly, it would be ideal if you have a passion for entertainment including movies, TV show and live events.

A day in the life
You will actively participate in the end-to-end software development lifecycle for the platforms and features we own - including requirements gathering, system design and implementation, testing and ongoing support. Our tech stack includes API gateway, Lambdas, S3, DynamoDB, model inference pipelines and complex caching techniques. Our team embraces agile methodologies with a focus on test automation and continuous deployment. Moreover, you will exchange ideas with your Product and Applied Science partner to influence and shape the future of our content personalization strategy.

About the team
Prime Video Discovery and Personalization Data Platform team owns of a set of services, APIs, offline datasets and push notifications. It streamlines ML and engineering teams to access and leverage high-quality customer data and signals (ex: impressions, channel signups) for offline processing and in real-time adhering to committed SLAs. We act as guardians and gatekeepers of the data creating data quality transparency for data consumers while collaborating with data sources to address problems at their root. We enable ML and engineering teams to focus on deriving value for end customers rather than resolving data inconsistencies.
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