Help us shape the future of Alexa where it not only responds to requests you make but also proactively anticipates what you might need next and informs you of it at the right moment. We are looking for a seasoned applied scientist to optimize several diverse voice and visual experiences across Amazon Echo products.
A day in the life
As a Senior Applied Science in Alexa Experiences and Devices, you will solve key challenges in how customers learn about and interact with voice assistants, and enable customers to get the most out of our products. You will design and develop fast, efficient, highly scalable machine learning algorithms. And work on problems spanning dialog systems, personalization, contextual bandits, recommender systems, reinforcement learning; developing novel solutions using large-scale real-world datasets. You will work with Alexa scientists, engineers and product team to build, evaluate and deliver solutions. You will develop innovative solutions enabling Alexa to learn from customer interactions to drive engagement.
About the hiring group
Our team is identifying the key drivers for engaging customers on the Alexa platform across devices, skills and services. As a part of the larger Alexa Customer Experience team, your area of influence and impact will be all of the Alexa organization across the globe. You will have a front row seat to the evolving voice assistant industry and opportunities to impact the customer experience of a cutting edge product used every day by people you know.
· Step in as an experienced member of an applied machine learning research team, establish technical credibility quickly, and help recruit elite machine learning practitioners.
· Contribute to setting up the research vision for the team, and develop and execute on a roadmap that addresses the major questions faced by the domains we serve.
· Raise the bar for research quality and impact. Stay up to date on research results both within and complementary to the Alexa technology space and helping both your team and the larger org utilize tried patterns and state of the art results to work smarter and faster.
· See it through — don’t stop at delivering a report or a model, use your work to influence and create durable change in how leaders at Alexa think about problems.
· Solve problems durably — design processes, tools, and programs that solve entire categories of problems without you or your team’s direct intervention.
· Hands-on practitioner - You yourself are actively involved in performing data analysis, A/B experimentation and modeling with large data sets to develop insights that increase device usage and customer experience.
· Work closely with product managers and software engineers to design experiments and implement end-to-end solutions. Communicate the results of your optimizations to various business stakeholders.
· Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences.
· Develop the skills of junior members through mentorship and training.
· Potentially support published works.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
· PhD degree with 4 years of applied research experience or a Masters degree and 6+ years of experience of applied research experience
· 3+ years of experience in building machine learning models for business application
· Experience programming in Java, C++, Python or related language
· PhD degree in Machine learning, computer science or related discipline.
· 1+ years’ experience in deploying Recommendation Systems using Contextual Bandits, Reinforcement Learning, Task Specific Embeddings, etc.
· Proficiency in Mxnet or Pytorch.
· Proficiency with Apache Spark.
· Hands-on applying statistical techniques such as hypothesis testing, experimental design or causal analysis.
· Strong communication and collaboration skills.