Applied Scientist: GenAI, NLP & Scalable ML

Audible; Newark (NJ); On-site; USD 172,400 - 223,400; Full time

Benefits offered by this job

RSUs

Sign-on payments

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Audible in Newark, NJ seeks an Applied Scientist to solve large-scale ML/AI problems and deploy production-grade models across NLP, GenAI, RL and distributed systems. You will collaborate with scientists, engineers, and product teams to deliver scalable solutions that impact millions of customers.

You will advance state-of-the-art techniques, contribute to research and ensure robust, data-driven decision-making across the platform.

Qualifications

• Knowledge of data structures, algorithms, statistics, and system design.
• Advanced degree or work-experience in ML, AI, NLP, GenAI, or related quantitative field.
• Experience with Deep Learning, NLP, GenAI or RL and production ML.
• Proficiency in Python, SQL and related scripting languages.
• Experience applying LLM/GenAI to real problems in production.

Responsibilities

• Understand business use cases and design scalable, automated solutions.
• Collaborate with scientists and engineers to productionize models.
• Review peer models to manage risk and improve customer experience.
• Develop techniques for Content Understanding, Recommendations and GenAI features.
• Contribute to fast-paced innovation and research initiatives.

Skills

Data structures

Algorithms

Statistics

System design

Python

SQL

LLMs/GenAI

Deep Learning

NLP

Reinforcement Learning

Education

MSc + 5 years of relevant experience

PhD + 1 year in related field

Tools

Python

Java

C++

AWS SageMaker

SQL

Job description

Audible in Newark, NJ seeks an Applied Scientist to solve large-scale ML/AI problems and deploy production-grade models across NLP, GenAI, RL and distributed systems. You will collaborate with scientists, engineers, and product teams to deliver scalable solutions that impact millions of customers.

You will advance state-of-the-art techniques, contribute to research and ensure robust, data-driven decision-making across the platform.