Applied Scientist Intern
Audible, Newark, NJ, United States
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 Intern at Audible, you will work alongside our science teams to solve problems spanning recommendation, content understanding, and AI-powered product experiences. 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. You'll apply ML/AI approaches to solve complex real-world problems while helping build the blueprint for how Audible works with AI.
ABOUT YOU
You are passionate about applying scientific approaches to real business challenges, with deep expertise in Machine Learning, Natural Language Processing, GenAI, and large language models. You thrive in collaborative environments where you can both build solutions and empower others to leverage AI effectively. You have a track record of developing production-ready models that balance scientific excellence with practical implementation. You're excited about not just building AI solutions, but also creating frameworks, evaluation methodologies, and knowledge management systems that elevate how entire organizations work with AI.
RESPONSIBILITIES
Design and implement innovative AI solutions across our three pillars: driving internal productivity, building the blueprint for how Audible works with AI, and unlocking new value through ML & AI-powered product features
Develop machine learning models, frameworks, and evaluation methodologies that help teams streamline workflows, automate repetitive tasks, and leverage collective knowledge
Enable self-service workflow automation by developing tools that allow non-technical teams to implement their own solutions
Collaborate with product, design and engineering teams to rapidly prototype new product ideas that could unlock new audiences and revenue streams
Build evaluation frameworks to measure AI system quality, effectiveness, and business impact
Mentor and educate colleagues on AI best practices, helping raise the AI fluency across the organization
QUALIFICATIONS
Experience programming in Java, C++, Python or related language
Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
Currently enrolled in a Master's or PhD program in Computer Science, Machine Learning, Statistics, NLP, or a related quantitative field
Coursework or project experience in at least one of: NLP, recommender systems, machine learning, or deep learning
Familiarity with ML frameworks (e.g., PyTorch, TensorFlow, HuggingFace)
Experience implementing algorithms using both toolkits and self-developed code
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ABOUT THIS ROLE
As an Applied Scientist Intern at Audible, you will work alongside our science teams to solve problems spanning recommendation, content understanding, and AI-powered product experiences. 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. You'll apply ML/AI approaches to solve complex real-world problems while helping build the blueprint for how Audible works with AI.
ABOUT YOU
You are passionate about applying scientific approaches to real business challenges, with deep expertise in Machine Learning, Natural Language Processing, GenAI, and large language models. You thrive in collaborative environments where you can both build solutions and empower others to leverage AI effectively. You have a track record of developing production-ready models that balance scientific excellence with practical implementation. You're excited about not just building AI solutions, but also creating frameworks, evaluation methodologies, and knowledge management systems that elevate how entire organizations work with AI.
RESPONSIBILITIES
Design and implement innovative AI solutions across our three pillars: driving internal productivity, building the blueprint for how Audible works with AI, and unlocking new value through ML & AI-powered product features
Develop machine learning models, frameworks, and evaluation methodologies that help teams streamline workflows, automate repetitive tasks, and leverage collective knowledge
Enable self-service workflow automation by developing tools that allow non-technical teams to implement their own solutions
Collaborate with product, design and engineering teams to rapidly prototype new product ideas that could unlock new audiences and revenue streams
Build evaluation frameworks to measure AI system quality, effectiveness, and business impact
Mentor and educate colleagues on AI best practices, helping raise the AI fluency across the organization
QUALIFICATIONS
Experience programming in Java, C++, Python or related language
Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
Currently enrolled in a Master's or PhD program in Computer Science, Machine Learning, Statistics, NLP, or a related quantitative field
Coursework or project experience in at least one of: NLP, recommender systems, machine learning, or deep learning
Familiarity with ML frameworks (e.g., PyTorch, TensorFlow, HuggingFace)
Experience implementing algorithms using both toolkits and self-developed code
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