Associate, ML Data Operations, GO-AI Operations

AI in this role

Perform human-in-the-loop data annotation tasks to train and validate machine learning models for Amazon Robotics.

annotation-tools

ai-data-labelingcomputer-visiondata-annotationimage-labelingvideo-annotationobject-detectionsemantic-segmentation

 Amazon Robotics develops state-of-the art robotics for Amazon’s Fulfillment Centers. Come build the future of human-technology interaction with us. We are combining computer vision, mobile robots, advanced end-of-arm tooling, and a high degree of freedom of movement to solve real-world problems on a huge scale. Our team collaborates with a variety of customers across Amazon worldwide to conceive, develop, prototype, and deploy a wide range of robotic systems. Within Amazon Robotics, Global Operations - Artificial Intelligence (GO-AI) enables Amazon to accelerate and scale our machine learning transformation via human-in-the-loop support.

As an Associate, ML Data Operations, you will be a vital Human-in-the-Loop expert in a non-technical role, responsible for executing data annotation tasks across highly diverse portfolios. Your work directly supports the training and validation of machine learning models for Amazon Robotics and Fulfillment Technologies, ensuring the quality and integrity of data which is critical for frontier AI improvements. Your work will involve analyzing processes such as packaging innovation, object manipulation, inventory storage, and sortation automation within fulfillment environments. This role requires strong attention to detail, the ability to make sound judgments using provided resources, and contributes to maintaining fulfillment center quality metrics through precise text/video/image annotation.

Key job responsibilities
This is a non-technical, operational role that requires adhering to process oriented, not programming skills. The Associate will perform precise annotation tasks, adhering to goals for accuracy (quality) and speed (productivity). The candidate is expected to demonstrate:
 
• Perform precise and consistent annotations across multiple data types (image, video, and text). This includes mastering techniques like object detection, semantic segmentation (pixel-level labeling), object tracking in video, and open-text evaluation.
• Proactively identify and correct errors, ensuring high data integrity and making obsessive precision crucial for model training.
• Leverage strong judgment to address ambiguous situations and, when guidelines fail, propose logical, consistent solutions that contribute to process improvement.
• Quickly learn and efficiently utilize various specialized annotation tools and platforms, adapting to new methodologies as required by evolving programs in domains like packaging, manipulation, storage, and sortation automation.
• Ability to scale and transition between multiple programs based on business needs
• This is a contract position with the potential to transition to a full-time employee role based on business needs.

A day in the life
We are looking for a Associate, ML Data Operations to undertake the task of foundational labelling functions, such as dialogue evaluation on speech, text, audio, and video data. Associates work in a 24x7 environment with rotational shifts. Associates would be working from home (VCC) with a 9-hour shift and with adequate network coverage typically with a reliable ISP (internet >=20 MBPS speed or higher as communicated from time to time) connection, either through DSL or a cable modem. The shift and break timings would be subject to change every 3-4 months or as per business requirement. In case an associate is working in night shift, night shift allowance will be provided as per applicable to Amazon’s work policy.

Associates who are hired to work from home should maintain (1) dedicated workspace i.e., table, chair & sufficient lighting (2) workspace / work related data shouldn’t be accessed by anyone other than employee

Weekly Offs: Rotational two-consecutive days off (it is a 5-day working week with 2 consecutive days off, not necessarily Saturday and Sunday) or as per business discretion.

Training Program: Selected candidates will participate in a structured one-week training program to develop essential capabilities and to ensure operational readiness prior to deployment

 

Weekly Offs: Rotational two-consecutive day off (it is a 5-day working week with 2 consecutive days off, not necessarily Saturday and Sunday) or as per business discretion.

Basic qualifications

- Bachelor's degree
- •Comfortable working in a collaborative environment with remote, multi-cultural teams, willing to share knowledge, and able to maintain individual productivity goals.
- •Show willingness to quickly adapt to new annotation methodologies and learn to use various specialized annotation tools
- •Good communication skills with the ability to articulate complex ideas and provide clear explanations.
- •Work in a flexible schedule/shift/work area.

Preferred qualifications

- Work a flexible schedule/shift/work area, including weekends, nights, and/or holidays
- • Experience in data annotation or similar high-volume, quality-focused roles

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.

How we score this

Associate, ML Data Operations, GO-AI Operations at Amazon scores 70 out of 100 on AI centrality, which makes it AI Level 3 of 4 (Works on AI) on this board. The level measures how much of the work is AI, not seniority.

Classification

AI Level 3. The daily work is on or around AI systems, without necessarily building the model: remove AI and the job is hollow.

AI Level 480 to 100
AI Level 360 to 79
AI Level 240 to 59
AI Level 10 to 39

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

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

AI Data LabelingComputer VisionData AnnotationImage LabelingVideo AnnotationObject DetectionSemantic SegmentationAnnotation Tools

Questions you could be asked

How do you keep labeling instructions consistent across a large annotation team?
Walk me through a computer vision problem you solved, from raw data to a deployed model.
Tell me about a project where data annotation was part of your work. What did you do?
Tell me about a project where image labeling was part of your work. What did you do?
Tell me about a project where video annotation was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: AI Data Labeling, Computer Vision, Data Annotation, Image Labeling, and Video Annotation. 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.
Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.

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