Catalog Specialist, Science Annontations

Amazon is hiring a Catalog Specialist, Science Annontations in Bellevue, United States. It pays $22-$31/hr and Level rates it ; you can apply on Level.

Level's read on this job

Builds AI ●●●● is how Level rates this role: the job is building AI systems.
Tools and skills the posting cites include Sagemaker and AI data labeling.
Level files it as a junior role.
Level lists 22,000+ other open roles at Amazon.
The role was posted within the past week.

AI in this role

sagemaker

ai-data-labeling

In the Worldwide Returns, ReCommerce & Sustainability (WW RR&S) group at Amazon, we are dedicated to ‘making zero happen’ – zero cost of returns, zero waste, and zero defects – to benefit our customers, company, and environment. We are an agile and inclusive organization that constantly innovates to create long-term value by investing in our people and our planet, not simply focusing on the bottom line.

WW R&R includes business, product, operations, data, and software engineering teams, who together manage the lifecycle of returned and damaged products. In WW R&R, you will partner across these teams to help customers discover great deals on quality used, rentals, and open box items; get the most value out of Amazon’s products; improve the customer returns experience; and reduce defects, waste, and cost in reverse logistics processes. You will be a leader, a builder, and an owner, collaborating cross-functionally with technical, operations, and business teams to design scalable and automated solutions to customer problems.

Amazon is Earth’s most customer-centric company and in WW R&R, the Earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns, ReCommerce & Sustainability team!
We are hiring an experienced Catalog Specialist to help us grow our business in innovative ways. In this role, you will work closely with our product, technology and science teams to support new Machine Learning (ML) models and data science classification algorithm development – all helping to delight our customers through new experiences throughout their Amazon shopping journey.

Key job responsibilities
• Work closely with our product, technology, and science teams to support Machine Learning (ML) models
• Perform data annotation required to train and evaluate ML models effectively
• Support data scientists in the development of classification algorithms
• Collaborate with cross-functional teams to ensure data annotation tasks align with project objectives and timelines
• Maintain high-quality standards for annotated data to optimize model performance
• Continuously evaluate and improve annotation processes to enhance efficiency and accuracy
• Strong analytical skills and the ability to deep-dive on complex problems
• Ability to manage multiple simultaneous projects requiring frequent communication, organization/time management and problem-solving skills

Basic qualifications

- Bachelor's degree
- Speak, write, and read fluently in English
- Experience with Microsoft Office products and applications
- 1+ years of proven experience in data annotation and labeling for ML model training and evaluation.

Preferred qualifications

- Experience in natural language data labeling, data annotation, linguistic annotation or other forms of data markup
- Experience working on the MTurk or Sagemaker platform for data annotation tasks
- Understanding of data annotation methodologies and tools
- Familiarity with Amazon's product and category ecosystem
- Previous exposure to machine learning concepts and algorithms
- Demonstrated ability to adapt to evolving technologies and methodologies in the ML domain

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The starting pay for this position is listed below. Final starting pay will be based on factors including experience, qualifications, and location. Starting Day 1 of employment, Amazon offers EAP, Mental Health Support, Medical Advice Line, 401(k) matching. Learn more about our benefits at https://hiring.amazon.com/why-amazon/benefits.

USA, WA, Bellevue - 21.58 - 30.72 USD hourly

How we rate this

Catalog Specialist, Science Annontations at Amazon rates 81 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Catalog Specialist, Science Annontations is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed pay is $22-$31/hr.
The role is based in US, WA, Bellevue.
The job was posted on October 15, 2026.
Seniority: junior.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI Data LabelingSagemaker

Questions you could be asked

How do you keep labeling instructions consistent across a large annotation team?
Walk me through how you've used Sagemaker in your day-to-day work.
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: AI Data Labeling and Sagemaker. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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