Senior Applied Scientist

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 a Senior 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) and Generative AI, Natural Language Processing (NLP), Reinforcement Learning (RL), real-time and distributed systems.

ABOUT YOU
Your work will focus on inventing and extending scientific approaches, models, and algorithms driven by customer needs at the product level, framing new research problems even when the problem is ill-defined and no textbook solution exists. You will lead the design, implementation, and delivery of scientifically complex, end-to-end solutions that are deployed into production, defining system-level requirements and writing a significant portion of the critical-path code. 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 independently with limited guidance, and your decision-making will consistently incorporate robust, data-driven business and technical judgment. You will drive your team’s scientific agenda, author internal or external peer-reviewed publications that validate the novelty of your work, mentor and develop other scientists, and build consensus across multiple teams. You will have the opportunity to innovate, invent, and think big, and influence the experiences of millions of customers. We are looking for a results-oriented Senior Applied Scientist with deep expertise in ML, NLP, Deep Learning, GenAI, and/or large-scale distributed computation.

As an Applied Scientist, you will...
- Understand complex, ambiguous use cases across the business and adopt/extend/design/invent solutions/models that are scalable, efficient, and automated, where neither the problem nor the solution is well defined
- Work closely with fellow scientists and software engineers (at Audible and Amazon) to build and productionize models, and 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
- Lead the design, development, and production deployment of scientifically complex, end-to-end solutions for Content Understanding, Recommendations, and GenAI-based product features, defining system-level requirements
- Drive and lead initiatives that employ the most recent advances in ML/AI/GenAI, drive your team’s scientific agenda, and author peer-reviewed publications
- Mentor and grow scientists on the team and across Amazon, and 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.

Basic qualifications

- Experience working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware, or experience in machine learning, data mining, information retrieval, statistics or natural language processing
- PhD plus 5+ years of research experience, or a Master’s degree plus 10+ years of research experience in one of the following disciplines: Machine Learning, Computer Science, Computer Engineering, Data Science, Applied Math, or a related quantitative field
- 5+ years of experience in Deep Learning, Natural Language Processing/Understanding, GenAI and/or Reinforcement Learning
- Experience with Python, SQL, other scripting languages
- Experience delivering science solutions into production systems

Preferred qualifications

- 3+ years of building machine learning models or developing algorithms for business application experience
- Author of publications at top-tier peer-reviewed conferences or journals, cited by other scientists
- Experience with building Recommendation Systems and/or large-scale content understanding systems
- Experience with large-scale online experimentation / A/B testing to validate science solutions in production
- Machine Learning Pipeline orchestration with AWS (SageMaker, Batch, Lambda, Step Functions) or similar cloud platforms
- Experience with Agile Software Development, and programming in at least one compiled language such as Java, C++, C#
- Experience mentoring scientists and leading science/design reviews across teams, and domain knowledge of comparable products (digital, retail)

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.