The Smart Home team is focused on making Alexa the user interface for the home. From the simplest voice commands (turn on the lights, turn down the heat) to use cases spanning home security, home entertainment, and the home environment, we are evolving Alexa into intelligent, indispensable companion that automates daily routines, simplifies interaction with appliances and electronics, and alerts when something unusual is detected.
You will be part of a team delivering features that are highly anticipated by media and well received by our customers. Here are a few links that highlight working with Alexa.
Meet the Alexa Software Team:
Charlie Kindel, Director, Alexa Smart Home, CEDIA Keynote:
Amazon Echo Emerges as a Surprise Leader in Smart Home Platform War:
You can make your Amazon Alexa Smarter:
How to make the Amazon Echo the Center of Your Smart Home:
As a Machine Learning Scientist, you will work with software developers and other teams to design and implement NLU models for how customers use and interact with smart devices in their homes. You will help lay the foundation to move from directed device interactions to learned behaviors that enable Alexa to proactively take action on behalf of the customer. And, you will have the satisfaction of working on a product your friends and family can relate to, and want to use every day. Like the world of smart phones less than 10 years ago, this is a rare opportunity to have a giant impact on the way people live.
•Master or PhD in Computer Science, Machine Learning, Statistics or a related quantitative field.
•2+ years of hands-on experience in applied machine learning, and predictive modeling and analysis.
•Algorithm and model development experience for large-scale applications
•Experience using Python, or other programming or scripting language, as well as with R, MATLAB.
•Solid understanding of foundational statistics concepts, NLU and ML algorithms: linear/logistic regression, random forest, boosting, GBM, NNs, etc.
•Experience with advanced ML models and concepts: HMMs, CRFs, MRFs, deep learning, regularization etc.
•Experience with machine learning pipelines for data from devices and sensors
•Experience working with modern tools for big data storage and analysis (e.g., AWS, Apache Spark, Hadoop, SQL, NoSQL)
•Experience setting up and using robust metrics for machine learning performance evaluation, A/B testing
•Experience with context-awareness, behavioral modeling, personalization
•Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.
•Proven track record in technically leading and mentoring scientists
Posted: October 19, 2018
Closes: December 18, 2018