AWS Machine Learning product teams are responsible for identifying customer needs and building products and services to meet those needs. At AWS, you will work side-by-side with product teams to build products and services designed with fairness and explainability in mind.
As a scientist on this team, you will:
· understand in depth the technical and scientific issues related to ML fairness and explainability,
· develop new solutions to solve problems in ML fairness and explainability,
· help define the strategies, priorities and metrics for ML fairness and explainability,
· help build and develop the AWS team, as needed, and
· liase with internal and external stakeholders.
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.
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.
· PhD degree in computer science, statistics, engineering, mathematics or another relevant discipline.
· 2+ years of experience using ML to solve problems in natural language processing (NLP), computer vision (CV), computer speech, or other AI domains. Alternately, 2+ years of experience solving problems in algorithmic fairness or explainability.
· Experience with machine learning applications.
· Experience working with software engineering teams.
· Passion for improving the fairness and explainability of ML systems.
· Willingness to engage with academic and other communities on issues of fairness and explainability.
· Excellent verbal and written communication skills, with demonstrated ability to synthesize large amounts of complex data and communicate complex concepts effectively to internal and external stakeholders.
· Ability to deliver in a fast-paced environment with shifting priorities and multiple stakeholders distributed across product, engineering and science teams.
· Willingness to travel.
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