About the position
As an Applied Scientist II, you will work on complex problems where neither the problem nor solution is well defined. You'll define and crisply frame research problems while developing novel scientific techniques in domains including machine learning, artificial intelligence (AI), natural language processing (NLP), large language models (LLMs), reinforcement learning (RL), and audio processing. Your primary focus will be on applying and extending existing scientific techniques, as well as inventing new approaches to address specific customer needs and business problems at the project level. You will contribute to internal or external peer-reviewed publications that validate the novelty of your work, while documenting and sharing findings in line with scientific best practices. You will work on LLM applications to enhance Audible's customer experience. We work in a highly collaborative environment where you'll primarily influence your team, begin mentoring more junior scientists, and partner with engineers and product managers to implement scalable, efficient approaches for difficult problems. You will operate with some autonomy while knowing when to seek direction to deliver high-quality scientific artifacts.
Responsibilities
- Define and implement scalable, efficient approaches for difficult problems related to audio storytelling and content experiences
- Apply and extend state-of-the-art LLM techniques to address specific customer or business needs at the project level
- Work on portions of systems, large components, applications, or services supporting machine learning and AI use cases
- Apply and extend state-of-the-art techniques in areas like NLP and deep learning to address specific customer or business needs
- Execute on team-level goals while creating intellectual property through your work
- Apply best practices in software development at the component level, ensuring solutions are testable, reproducible, and efficient
- Document and share findings that contribute to the internal and external scientific community
- Begin mentoring and developing teammates while gaining experience in tactical work and learning to be strategic
- Collaborate with tech and product teams to implement solutions that consider relevant tradeoffs at the component level
