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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

This is a rare opportunity to shape the future of audio content discovery and understanding at a massive scale. As the PE for Audible's AI Foundations, you will be at the forefront of designing next-generation infrastructure and capabilities that will directly impact the daily entertainment experience of millions of customers.

You will have the chance to work with AI technologies, including large language models, multi-modal representation learning, and real-time personalization systems. By architecting the core AI services that power Audible's content discovery, recommendation, and conversational experiences, your work will be at the heart of delivering innovative and engaging experiences to users. Beyond the technical challenges, this role offers the unique ability to influence the strategic direction of Audible's AI initiatives, collaborating closely with cross-functional teams and driving the evolution of the company's AI capabilities. If you're passionate about building robust, scalable, and innovative AI systems that can transform the way customers interact with digital content, this is an unparalleled opportunity to make a lasting impact.

ABOUT THE TEAM

Our team is responsible for the foundational AI infrastructure and services that power Audible's key customer experiences, including content discovery, personalization, and conversational AI. We oversee the development and operation of a diverse portfolio of systems that sit at the intersection of machine learning, natural language processing, and large-scale distributed architectures. The current state of our existing systems reflects both the progress we've made and the challenges we now face as we strive to deliver increasingly sophisticated AI capabilities to our customers. Our core offerings include:

Content AI Infrastructure:

• Semantic search and content understanding systems that need evolution to better handle audio-centric content

• Content processing pipelines for attribute extraction and organization from various sources including customer reviews and metadata

• Machine learning serving infrastructure for recommendation and personalization models requiring optimization for real-time, interactive AI experiences

AI Foundation Services:

• Large language model integration services for conversational AI and content understanding

• Content understanding pipelines for processing multi-modal data and creating rich content representations

• Feature management infrastructure for machine learning model support

ABOUT YOU

You’re in your element when you’re being challenged, and you’re always eager to share your ideas, take on responsibility and keep developing at an exciting pace. We look for people who show initiative, set their standards high and see every failure as an opportunity to learn. You’re looking for an environment where you can thrive and help your team reach their potential. With opportunities to challenge yourself and lead a talented team to success, you’ll find what you’re looking for at Audible.

As a Lead Software Development Engineer, you will be responsible for:

o Architecture & Design:

• Designing the scalable, low-latency architecture for integrating large language models (LLMs) into our conversational AI and content understanding systems. This includes optimizing for streaming responses, efficient context management, and robust prompt engineering frameworks.

• Creating the next-generation content processing and representation learning pipelines that can seamlessly handle the multi-modal nature of Audible's catalog, including text, audio, and associated metadata.

• Defining the overarching integration patterns and service mesh architecture that will enable the smooth flow of data and features across our diverse AI-powered systems.

o Infrastructure Evolution:

• Architecting the high-performance, real-time feature computation infrastructure that will power our personalization and recommendation models, ensuring sub-millisecond latency and consistent data freshness.

• Designing the evaluation and quality assurance frameworks that will be used to assess the performance, accuracy, and reliability of our AI systems, including mechanisms for model versioning, A/B testing, and continuous monitoring.

• Establishing the technical governance structure and best practices that will guide the development and deployment of Audible's AI foundation services across the organization.

o Technical Strategy:

• Crafting the long-term technical roadmap for the evolution of Audible's AI platform, aligning it with the company's strategic vision and customer experience goals.

• Influencing and collaborating with cross-functional teams in Data Science, Product, and Engineering to ensure the seamless integration of AI capabilities into Audible's overall technology strategy.

• Defining the architectural standards, design patterns, and implementation guidelines that will be used to maintain consistency and reliability across Audible's AI infrastructure.

o Operational Excellence

• The Principal Engineer will be a key driver of operational excellence across Audible's AI foundation services, establishing robust frameworks and best practices for system reliability, performance, and scalability. They will architect comprehensive monitoring and observability solutions to ensure early detection of potential issues, implement sophisticated alerting mechanisms, and design automated recovery processes.

• The PE will be responsible for creating resilient systems that maintain high availability while managing complex dependencies across the AI infrastructure. This includes establishing SLAs, defining performance metrics, and building automated testing frameworks to validate system behavior under various conditions. They will need to implement efficient capacity planning strategies, optimize resource utilization, and ensure cost-effective scaling of AI services. Additionally, the PE will establish incident management protocols, lead post-mortem analyses, and drive continuous improvement initiatives to enhance system reliability and operational efficiency. Their focus on operational excellence will be crucial in maintaining the high-performance, reliable infrastructure necessary to serve millions of customers while enabling rapid innovation and deployment of new AI capabilities.

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.

For basic qualifications enter as freeform, no *

Bachelors degree in Computer Science, Electrical Engineering, or a related STEM field

10+ years in software engineering

Previous experience with on-device AI/ML optimization, including model deployment, quantization, and performance tuning

Same for preferred

Distributed Systems Architecture: A deep understanding of distributed systems design patterns, including scalable data storage, efficient caching strategies, and robust failure handling mechanisms. This expertise will be crucial in architecting the next-generation of Audible's content processing and ML serving infrastructureMachine Learning Infrastructure: Extensive experience in building and operating large-scale ML platforms, with expertise in areas such as model versioning, automated deployment, feature management, and real-time inference. This knowledge will be critical in evolving Audible's ML platform to support the growing demands of AI-powered content discovery and personalization.Natural Language Processing: Solid grasp of NLP techniques and architectures, particularly in the context of content understanding, semantic search, and multi-modal representation learning. The PE must be well-versed in the latest advancements in LLM integration, prompt engineering, and cross-modal alignment to drive Audible's content understanding capabilities.Real-time Serving Systems: Proficiency in designing low-latency, high-throughput serving systems capable of handling the stringent performance requirements of Audible's customer-facing AI features. This includes expertise in areas like load balancing, caching, and dynamic model routing.Content Recommendation Systems: Deep understanding of content personalization and recommendation architectures, with the ability to create innovative approaches to model content relationships, capture user preferences, and drive engaging content discovery experiences.

Basic Qualifications

* Bachelors degree in Computer Science, Electrical Engineering, or a related STEM field

* 10+ years in software engineering

* Previous experience with on-device AI/ML optimization, including model deployment, quantization, and performance tuning

Preferred Qualifications

* Distributed Systems Architecture: A deep understanding of distributed systems design patterns, including scalable data storage, efficient caching strategies, and robust failure handling mechanisms. This expertise will be crucial in architecting the next-generation of Audible's content processing and ML serving infrastructure

* Machine Learning Infrastructure: Extensive experience in building and operating large-scale ML platforms, with expertise in areas such as model versioning, automated deployment, feature management, and real-time inference. This knowledge will be critical in evolving Audible's ML platform to support the growing demands of AI-powered content discovery and personalization.

* Natural Language Processing: Solid grasp of NLP techniques and architectures, particularly in the context of content understanding, semantic search, and multi-modal representation learning. The PE must be well-versed in the latest advancements in LLM integration, prompt engineering, and cross-modal alignment to drive Audible's content understanding capabilities.

* Real-time Serving Systems: Proficiency in designing low-latency, high-throughput serving systems capable of handling the stringent performance requirements of Audible's customer-facing AI features. This includes expertise in areas like load balancing, caching, and dynamic model routing.

* Content Recommendation Systems: Deep understanding of content personalization and recommendation architectures, with the ability to create innovative approaches to model content relationships, capture user preferences, and drive engaging content discovery experiences.

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