Applied Scientist

Description

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 inventing or adapting scientific approaches, models, and algorithms driven by customer needs at the project level. You will develop components and/or end-to-end solutions that are deployed into production or directly support production systems, delivering consistently high-quality work that meets both scientific and engineering best practices. 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. You will work semi-autonomously to deliver solutions, contribute to research papers at peer-reviewed venues when appropriate, and document your work thoroughly to enable others to understand and reproduce it. 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 and think big within your projects scope, implement cutting-edge optimization services and algorithms, and influence the experiences of millions of customers. We are looking for a results-oriented Applied Scientist with deep knowledge in ML, NLP, Deep Learning, GenAI, and/or large-scale distributed computation.

As an Applied Scientist, you will...

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  Understand use cases across the business and adopt/extend/design/invent solutions/models that are scalable, efficient, and automated for difficult problems that are not well defined

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  Work closely with fellow scientists and software engineers (at Audible and Amazon) to build and productionize models, deliver novel and highly impactful features

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  Review models of peers for the purpose of reducing and managing risk to the business, while improving customer experience

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  Design, develop, and deploy modeling techniques that focus on Content Understanding, Recommendations, GenAI-based application, by employing a wide range of methodologies, working from simple to complex

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  Contribute to initiatives that employ the most recent advances in ML/AI in a fast-paced, experimental environment

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  Push the boundary of innovation

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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.

Basic Qualifications

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  MSc + 5ys of relevant experience, or PhD in one of the following disciplines: Computer Science, Computer Engineering, Machine Learning, Data Science, Applied Math, or a related quantitative field

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  Understanding of data structures, algorithms, software development

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  3+ years of experience in Deep Learning, Natural Language Processing/Understanding, Reinforcement Learning and/or GenAI

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  Proficiency in Python, SQL, other scripting languages

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  Experience employing and innovating with LLMs/GenAI to solve complex problems

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Preferred Qualifications

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  2+ years of industry experience in with ML/AI projects in addition to PhD

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  Experience with building Recommendations Systems

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  Machine Learning Pipeline orchestration with AWS (SageMaker, Batch, Lambda, Step Functions) or similar cloud-platforms

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  Experience with Agile Software Development

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  Experience with programming in at least one compiled programming language such as Java, C++, C#

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  Publications at top-tier peer-reviewed conferences or journals

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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 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 $136,000/year in our lowest geographic market up to $223,400/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 . This position will remain posted until filled. Applicants should apply via our internal or external career site.