Applied Scientist I - Machine Learning and AI, Amazon Music Catalog Team
Content + Source + Freshness • 14 Dec 2025 • 95% confidence
Offer value
Valuable due to potential for impactful contributions and learning opportunities within a global enterprise.
- Work with industry-leading methodologies in AI.
- Engage with large-scale data affecting customer success.
- Collaborate within a dynamic team environment.
Pros
- Engagement with large datasets within a reputable global brand.
- Opportunities for continuous learning and using innovative techniques.
- Fostering an environment that encourages creativity and collaboration.
Cons
- Requires a robust technical background and experience.
- Possible pressure to deliver results rapidly.
- Collaborative elements may require balancing multiple viewpoints.
Who it's for
Entry to Mid-level • Flexible/Remote
Good fit
- Entry to mid-level professionals in machine learning.
- Those fascinated by data-driven decision making.
- People eager to learn and innovate in technology.
Not recommended for
- Highly experienced professionals seeking senior roles.
- Candidates seeking traditional work environments.
- Individuals without a strong analytical focus.
Motivation fit
Key skills
About the job
Description
Are you passionate about applying machine learning and data-driven techniques to solve real-world problems at global scale? Amazon is seeking an Applied Scientist who combines curiosity, creativity, and strong analytical skills to build models and algorithms that power customer experiences and business decisions.
As an Applied Scientist I, you will work with senior scientists and engineers to design, train, and deploy ML models using large-scale datasets. You will experiment with modern techniques in supervised and unsupervised learning, natural language processing, computer vision, or optimization—depending on the team’s focus area. You’ll also have opportunities to learn Amazon’s scalable infrastructure, experiment platforms, and science best practices.
This role is ideal for someone early in their career who enjoys working in collaborative, multidisciplinary teams and is excited by the opportunity to learn, innovate, and deliver measurable impact to customers.
Key responsibilities include:
Collaborate with scientists, engineers, and product managers to define and frame business problems as ML or optimization tasks.
Build, train, and evaluate models using large, complex datasets.
Implement scalable data pipelines and model-serving systems.
Analyze experimental results, draw insights, and refine models to improve accuracy and robustness.
Communicate findings and recommendations to technical and non-technical audiences.
Continuously learn and apply new algorithms and techniques to improve existing systems.
