Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.

The Sponsored Brands Advertiser Control team is a versatile environment, with a wide variety of challenges. We guide advertisers to make informed decisions by sharing insights and forecasts. We help advertisers deliver effective campaigns automatically by optimizing bids and targets on behalf of them. We enable advertisers to achieve brand advertising goals with maximum efficiency. We look at mega size data from both retail and advertising space, and coming up with ML and DL based solutions through multiple products.

We are looking for a core team member to help solve some of our most challenging problems that involve processing Amazon-scale big data, building out high quality systems to support public facing forecasting, recommendation and optimization APIs, model backed online services and machine learning pipelines.

Key job responsibilities
As a SDE in the team, you will:
- Design and build innovative machine learning and deep learning systems utilizing the latest technologies such as LLM to provide personalized targeting, real-time forecasting and recommendations
- Develop machine learning systems to automate bidding, targeting and budgeting on behalf of advertisers
- Design and implement online experiments that affect hundreds of millions of customers and demonstrate the impact of the features and products you develop in a measurable way
- Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving
- work closely with applied scientists, peer engineers to optimize the performance of machine-learning models and infrastructure, and implement end-to-end solutions.