Machine Learning Scientist, AWS Kumo

Job summary
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 Processing 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

We are working to achieve our business goals by deriving insights from a wealth of datasets like the AWS service metrics and logs, chat and call audio logs, email transcripts, support agent and support case data, customers context and sentiment, and AWS knowledge articles, tools and workflows.

Inclusive Team Culture
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.

Work/Life Balance
Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.

Mentorship & Career Growth
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.

Basic Qualifications

• Masters degree in computer science, mathematics or related discipline
• 2+ years of experience building machine learning systems for production use is required
• Experience with fast prototyping
• Experience with machine learning/deep learning frameworks/libraries like AWS SageMaker, TensorFlow, PyTorch, MXNet
• Hands-on development experience in one of the programming languages like Python, Java, C++
• Experience working effectively with software engineering teams

Preferred Qualifications

• PhD with specialization in machine learning/deep learning
• Experience or working knowledge of NLP and deep learning
• Strong verbal and written communication with experience presenting complex technical information to technical and non-technical audiences
• The ability to invent, a track record of thought leadership and contributions that have advanced the field.

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.