At AWS, we want to be able to identify every need of a customer across all AWS services before they have to tell us about it, and then find and seamlessly connect them to the most appropriate resolution for their need, eventually fulfilling the vision of a self-healing cloud. We are looking for Machine Learning Scientists / Applied Scientists / ML Scientists with unfettered curiosity and drive to help build “best in the world” support experience that customers will love!
You will have an opportunity to lead, invent, and design tech that will directly impact every customer across all AWS services. We are building industry-leading technology that cuts across a wide range of ML techniques from Natural Language Understanding to Deep Learning. You will be a key driver in taking something from an idea to an experiment to a prototype and finally to a live production system.
We are a newly formed team, and we pack a punch with principal level engineering, science, product, and leadership talent. We have started building the brightest science team and you have the opportunity to lead and establish a culture for the big things to come. We combine the culture of a startup, the innovation and creativity of a R&D Lab, the work-life balance of a mature organization, and technical challenges at the scale of AWS. We offer a playground of opportunities for builders to build, have fun, and make history!
ML Scientist / Applied Scientist MAJOR RESPONSIBILITIES
- Deliver real world production systems at AWS scale.
- Work closely with the business to understand the problem space, identify the opportunities and formulate the problems.
- Use machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems.
- Analyze and extract relevant information from large amounts of data and derive useful insights.
- Work with software engineering teams to deliver production systems with your ML models
- Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation