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

The playback team at Audible owns the core playback logic end-to-end. All our services, systems and players are invoked each time customers click play (or download a title for offline playback) to enjoy the best playback experience.

Specifically we own 1) Core playback logic on players such as Android, iOS and Web, 2) Playback services related to content delivery infrastructure, security & digital rights management, and processing / synchronization of listening data across devices, 3) listening statistics that power royalties, returns, creator metrics, and more, 4) Playback metrics, insights and platform, obsessing over the core playback metrics and trends that represent the service health of Audible Playback and delivery, 5) Unified Audible Playback SDK, that can be shared with both 3P and 1P customers, 6) Player optimizations related to QoE, ABR and Quality / CDN selection across Android, iOS and web, Server optimizations to segment the customer data and tune playback parameters in real-time, 7) all of the efforts around quality of experience monitoring which enable timely, accurate and automated detection of QoS performance degradations and identify opportunities to improve playback experience, 8) automated ML based review and sentiment analysis system that will proactively analyze posts across social media feeds, customer reviews to automatically establish relational models to identify and cross correlate issues detected by tools such as Anomaly detection, and finally, 9) Driving Audible’s playback initiatives to establish faculty collaborations to advance state of the art in Internet Audio.

ABOUT YOU

As an applied scientist on our team, you will wear many hats and work in a highly collaborative environment that’s more startup than big company.

You'll need to tackle complex problems that span a variety of domains : Machine learning, Artificial intelligence, Natural Language processing, real-time and distributed systems and help us build services and systems from the ground up which scale and serve billions of requests per day, with obsessively high reliability and low operational overhead.

You'll have an opportunity to explore, innovate, invent and simplify various Playback state-of-art services and algorithms which leverage both custom and Industry proven Machine Learning, Natural Language processing and Artificial Intelligence technologies.

Your work will focus on training and evaluating models and deploying them to production where we continuously monitor and evaluate.

You'll work on large engineering efforts that solve significantly complex problems facing global customers. You'll be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction.

You must be equally comfortable digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models.

You consistently bring strong, data-driven business and technical judgment to decisions. We experiment a lot and it is a must to learn and be curios.

You'll be encouraged to see the big picture, be innovative, and positively impact millions of customers.

As a Senior Applied Scientist, you will...

• Understand large complex use cases across the Playback org and design scalable, efficient, and automated solutions
• Design, develop, and deploy state-of-art Optimization services & algorithms and novel adaptive bitrate algorithms
• Partner closely with other Amazon scientists focused on streaming insights and optimization use cases
• Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features
• Push the boundary of innovation
• Mentor and grow the scientists in the team and across Amazon

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

• 5+ years of industry experience in Deep learning, Natural Language Processing / Understanding, Reinforcement learning and Speech processing
• PhD in one of the following disciplines : Computer Science, Machine Learning, Statistics, Data Science, Applied Math, Operational Research or a related quantitative field
• Fluency in Python, SQL or similar scripting languages and skilled at Java, C++, or other programing languages
• Experience in algorithm development
• Depth and breadth in state-of-the-art machine learning technologies
• Machine Learning Pipeline orchestration with AWS (SageMaker, Batch, Lambda, Step Functions) or similar cloud-platforms
• Big Data Engineering with Spark / AWS EMR & Glue

PREFERRED QUALIFICATIONS

• Domain knowledge of comparable products (digital, retail)
• Publications at top-tier peer-reviewed conferences or journals in one of those areas (natural language processing / understanding, deep learning, machine learning, or speech processing)
• Proven track record of innovation in creating novel algorithms and advancing the state of the art

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.

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