At Audible, we believe stories have the power to transform lives. It’s why we work with some of the world’s leading creators to produce and share audio storytelling with our millions of global listeners. We are dreamers and inventors who come from a wide range of backgrounds and experiences to empower and inspire each other. Imagine your future with us.
ABOUT THIS ROLE
As an Applied Scientist, you will solve large complex real-world problems at scale, draw inspiration from the latest science and technology to empower undefined/untapped business use cases, delve into customer requirements, collaborate with tech and product teams on design, and create production-ready models that span various domains, including Machine Learning (ML), Artificial Intelligence (AI), Natural Language Processing (NLP), Reinforcement Learning (RL), real-time and distributed systems.
ABOUT YOU
Your work will focus on designing, developing and implementing solutions for scientific problems by primarily applying or extending known techniques, but also aiming to go beyond, building innovative end-to-end solution in production and having scientific contributions. You will develop reusable science components and services that resolve architecture deficiencies and customers’ pain points, while making technical trade-offs for long-term/short- term. Your decision-making will consistently incorporate robust, data-driven business and technical judgment. You will collaborate with other scientists to raise the bar of both scientific and engineering complexity for the team and to foster valuable scientific partnership opportunities to help/guide science decisions. We work in a highly collaborative, fast-paced environment where scientists, engineers, and product managers work to test and build scalable foundational capabilities, as well as customer facing experiences. You will have the opportunity to innovate, invent, think big, and streamline cutting-edge optimization services and algorithms to influence the experiences of millions of customers. We are looking for a motivated, results-oriented Applied Scientist with strong rigor and demonstrable skills in ML, NLP, Deep Learning, GenAI, and/or large-scale distributed computation.
As an Applied Scientist, you will...
- Understand use cases across the business and design solutions/models that are scalable, efficient, automated
- Work closely with fellow scientists and software engineers (at Audible and Amazon) to build and productionize models, deliver novel and highly impactful features
- Review models of peers for the purpose of reducing and managing risk to the business, while improving customer experience
- Design, develop, and deploy modeling techniques, with a focus on Content --Understanding and Recommendations, employing a wide range of methodologies, working from simple to complex
- Contribute to initiatives that employ the most recent advances in ML/AI in a fast-paced, experimental environment
- Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and impactful features
- Push the boundary of innovation
ABOUT AUDIBLE
Audible is the leading producer and provider of audio storytelling. We spark listeners’ imaginations, offering immersive, cinematic experiences full of inspiration and insight to enrich our customers daily lives. We are a global company with an entrepreneurial spirit. We are dreamers and inventors who are passionate about the positive impact Audible can make for our customers and our neighbors. This spirit courses throughout Audible, supporting a culture of creativity and inclusion built on our People Principles and our mission to build more equitable communities in the cities we call home.- MSc in one of the following disciplines: Computer Science, Computer Engineering, Machine Learning, Data Science, Applied Math, or a related quantitative field
- Experience in understanding of standard data structures, algorithms, and software development
- Fluency in Python, SQL or similar scripting languages
- Algorithm development experience
- Breadth in ML technologies
- Experience in employing LLMs/GenAI to solve problems- PhD in one of the following disciplines: Computer Science, Computer Engineering, Machine Learning, Data Science, Applied Math, or a related quantitative field
- 1+ years of industry experience in Deep Learning, Natural Language Processing/Understanding, Reinforcement Learning and/or GenAI
- Machine Learning Pipeline orchestration with AWS (SageMaker, Batch, Lambda, Step Functions) or similar cloud-platforms
- Experience with Agile Software Development
- Experience with programming in at least one compiled programming language such as Java, C++, C#
- Publications at top-tier peer-reviewed conferences or journals
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit
https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $129,400/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit
https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
