- Seattle, WA
Do you want to join a brand-new team building an AI system that would disrupt the industry? Do you enjoy dealing with ambiguity and working on hard problems in a fast-paced environment?
Amazon Connect is a highly disruptive cloud-based contact center that enables businesses to deliver engaging, dynamic, and personal customer service experiences. With Amazon Connect, you can create your own cloud-based contact center and be taking calls in minutes. Amazon Connect leverages the power of Artificial Intelligence and the large ecosystem of AWS services such as Lex, Polly, Lambda, S3, and Kinesis to provide a truly frustration free and natural customer experience. With this technology, we are transforming an industry and the way customers interact with businesses and how agents service them.
As an Applied Scientist on our team, you will analyze data from huge data sets, create ML models from conception to deployment, and work closely with other senior technical leaders within the team and across AWS. You will demonstrate your deep Applied Science knowledge and experience at prototyping and building accurate and effective ML models using technology such as AWS Sagemaker, PyTorch, and SparkML. Our team is at an early stage, so you will have significant impact on our ML deliverables with no operational load from existing models/systems.
We have a rapidly growing customer base and an exciting charter in front of us that includes solving highly complex engineering and algorithmic problems. We are looking for passionate, talented, and experienced people to join us to innovate on this new service that addresses customer needs to build modern contact centers in the cloud. The position represents a rare opportunity to be a part of a fast-growing business soon after launch, and help shape the technology and product as we grow. You will be playing a crucial role in developing the next generation contact center, and get the opportunity to design and deliver scalable, resilient systems while maintaining a constant customer focus.
Learn more about Amazon Connect here:
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. Amazon's culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
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.
• Master's degree in Machine Learning, Computer Science, or in a highly quantitative field or equivalent years of experience.
• Programming experience with at least one modern language such as Python, Java, C++, or C# including object-oriented design
• 3+ years of experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems.
• 4+ years of hands-on experience performing big data analysis and prototyping/building ML models using Python (with PyTorch, Pandas, or other ML libraries) and/or Scala/Java (with SparkML or other ML frameworks).
• Wrote scientific papers explaining ML design for solving ambiguous problems and describing model performance using standard performance metrics.
• PhD in Machine Learning, CS, or in a highly quantitative field.
• Experience with using ML frameworks/platforms such as AWS Sagemaker, Tensorflow, etc.
• Experience with distributed computation frameworks such as Elastic Map Reduce and Apache Spark.
• Built ML classification or regression models solving ambiguous, complex problems with some guidance. The resulting models were innovative, scalable, and maintainable.
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, visit https://www.amazon.jobs/en/disability/us
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