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Flexible Amazon Data Annotation information

What are the key skills and qualifications needed to thrive as a Flexible Amazon Data Annotation Specialist, and why are they important?

To thrive as a Flexible Amazon Data Annotation Specialist, you need strong attention to detail, analytical skills, and the ability to accurately label and categorize data, often supported by at least a high school diploma or equivalent. Familiarity with Amazon's proprietary annotation platforms, basic data management tools, and sometimes workflow tracking systems is typically required. Excellent communication, time management, and the ability to work independently make someone stand out in this position. These skills ensure high-quality, consistent data labeling, which is crucial for training reliable machine learning models and supporting Amazon’s AI-driven products.

What are some common challenges faced in a Flexible Amazon Data Annotation role, and how can I prepare for them?

In a Flexible Amazon Data Annotation role, one common challenge is maintaining high accuracy while working with large volumes of data, often under tight deadlines. Adapting to evolving project guidelines and learning to use proprietary annotation tools efficiently are also typical hurdles. To prepare, familiarize yourself with data labeling best practices, develop strong attention to detail, and practice time management to ensure consistent productivity. Being proactive in seeking clarification when guidelines change and participating in any available training can also help you excel in the role.

What is a Flexible Amazon Data Annotation job?

A Flexible Amazon Data Annotation job involves labeling, categorizing, or identifying data (such as text, images, or videos) to help train artificial intelligence and machine learning models used by Amazon. Workers perform these tasks remotely and can often choose their own hours, making the job flexible and suitable for people seeking part-time or supplemental work. The tasks may include tagging products, categorizing content, or transcribing information, depending on the project requirements. Attention to detail and accuracy are important, as annotated data directly impacts the performance of Amazon's AI systems.

What is the difference between Flexible Amazon Data Annotation vs Data Labeler?

AspectFlexible Amazon Data AnnotationData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote, flexible hoursRemote or on-site, flexible hours
Industry UsageAmazon and e-commerce platformsVarious industries including e-commerce, AI training
Job FocusAnnotating data for Amazon AI modelsLabeling data for machine learning models

Flexible Amazon Data Annotation involves annotating data specifically for Amazon's AI systems, often requiring familiarity with Amazon's platform. Data Labelers perform similar tasks across multiple industries, focusing on preparing data for machine learning. Both roles are remote, entry-level, and involve data annotation, but Flexible Amazon Data Annotation is more specialized for Amazon's ecosystem.

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Flexible Amazon Data Annotation jobs near you

Operations Lead - Perception Data Labeling

Zoox

Foster City, CA

$163K - $223K/yr

Full-time

Medical, Life, PTO

Posted 28 days ago

Job description

We are seeking an experienced and highly skilled operations leader to join the Perception Data and Labeling team. The team is responsible for training and evaluation data powering the perception (vision, lidar, and other modalities) ML models at Zoox. The candidate will work alongside ML engineers, product owners, data engineers, and external partners to scope and deliver high-quality training and evaluation data to help the Zoox Perception AI team meet critical deadlines.
In this role, you will:

  • Translate  and coalesce company-wide feature requirements into concrete and comprehensive data deliverables

  • Work collaboratively with AI teams, project management, and data annotation teams to manage the collection and labeling of training and evaluation data powering Zoox's AI perception stack. 

  • Manage vendor allocation and budgeting in conjunction with the milestone and release timelines at Zoox. 

  • Define and maintain clear tracking of outcomes, risk, and data quality to ensure transparency and accountability.

Qualifications:
  • Experience in Technical Program Management or Operations Strategy: 5+ years of experience managing complex, cross-functional programs within the technology, autonomous driving, or AI/ML sectors.
  • Perception & Sensor Domain Knowledge: Strong understanding of the machine learning development lifecycle (MLDL) and familiarity with autonomous vehicle sensor modalities (LiDAR, Radar, Camera, Thermal) and their respective data annotation requirements.
  • Strategic Capacity Planning: Demonstrated ability to translate high-level product roadmaps and engineering requirements into granular operational execution plans. Experience managing resource allocation and throughput for large-scale workflows.
  • Vendor Ecosystem Understanding: Experience overseeing large-scale vendor operations or managed service providers, specifically regarding quality, latency, and volume targets.
  • Data Literacy: Proficiency with SQL or Python/Pandas to query databases, analyze throughput metrics, and generate reports without needing engineering support.
$163,000 - $223,000 a year

Base Salary Range

There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.

Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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