Applied Scientist I, Ads Trust
Technology, Data & Digital · Data, AI & Analytics · Data Science · Machine Learning · Artificial Intelligence
In short
The Ads Trust Science team in Bangalore is seeking an Applied Scientist I to build and develop ML models for content understanding in Ads. This role involves working with large-scale data and collaborating with engineers to deploy production-level code for ad moderation. Expertise in machine learning, deep learning, and programming is required.
Responsibilities
- Build and develop ML models to address content understanding problems in Ads.
- Utilize a variety of visual and textual features for model development.
- Collaborate with engineers and other scientists to build, train, and deploy models.
- Develop production-level code enabling moderation of millions of ads daily.
Requirements
- Experience programming in Java, C++, Python or related language.
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse.
- Experience building machine learning models or developing algorithms for business application.
- Experience researching about machine learning, deep learning, NLP, computer vision, data science.
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning.
Desired Qualifications
- Are enrolled in or have completed a Bachelor's degree in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields.
- Experience applying theoretical models in an applied environment.
- Have publications at top-tier peer-reviewed conferences or journals.
- Master's degree.
Benefits
- 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.
#machine learning#ads trust#content understanding#computer vision#nlp