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Flexible Amazon Data Annotation Jobs (NOW HIRING)

Teleskope

Jr. AI Engineer - Data Annotation

New York, NY · On-site

$75K - $90K/yr

What You'll Do * Do hands-on data annotation and quality control (labeling, reviewing, and ... Flexible vacation and work-from-home days. * Competitive salary and meaningful equity. * Health ...

Encord

Strategic Projects Lead

New York, NY · On-site

Own data annotation projects end-to-end, translating complex AI/ML requirements into clear ... Flexible PTO to fully recharge * Annual learning & development budget * Comprehensive health ...

Encord

Strategic Projects Lead

New York, NY · On-site

$150K - $300K/yr

Own data annotation projects end-to-end, translating complex AI/ML requirements into clear ... Flexible PTO to fully recharge * Annual learning & development budget * Comprehensive health ...

Encord

Strategic Projects Lead

San Francisco, CA · On-site

Own data annotation projects end-to-end, translating complex AI/ML requirements into clear ... Flexible PTO to fully recharge * Annual learning & development budget * Comprehensive health ...

Amazon

Data Engineer, Amazon Ads

New York, NY · On-site

$125K - $150K/yr

... with the Amazon data stack - Datanet/ETLM, Cradle, Andes 3.0, Redshift Spectrum, EDX, and ... Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid ...

Amazon

Data Engineer, Amazon Ads

Seattle, WA · On-site

$130K - $156K/yr

... with the Amazon data stack - Datanet/ETLM, Cradle, Andes 3.0, Redshift Spectrum, EDX, and ... Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid ...

Jobgether

$40 - $54/hr

Our partner is looking for a #49426 Video Annotation & Data Labeling Project based in Netherlands ... Flexible task-based work within a long-term AI data project. * Payments processed securely through ...

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Flexible Amazon Data Annotation information

What is a flexible Amazon data annotation?

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 are the key skills and qualifications needed to thrive as a flexible Amazon data annotation specialist?

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 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.

Can I work for flexible Amazon data annotation with no experience?

Flexible Amazon data annotation roles typically do not require prior experience, as they often involve simple tasks like image labeling or data categorization. Basic computer skills and attention to detail are usually sufficient, and training is often provided to new workers. However, some tasks may have specific requirements or preferred skills, so reviewing the job listing is recommended.

How much do flexible Amazon data annotation jobs pay?

Flexible Amazon data annotation jobs typically pay between $10 and $15 per hour, depending on the complexity of the tasks and the platform used. Pay rates can vary based on experience, the specific project, and whether the work is freelance or through a staffing agency.
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Flexible Amazon Data Annotation jobs near you
Infographic showing various Flexible Amazon Data Annotation job openings in the United States as of August 2026, with employment types broken down into 50% Full Time, and 50% Part Time. Highlights an 100% In-person job distribution.

Annotation Operations Manager

DYNA Robotics Inc

Redwood City, CA • On-site

Full-time

Posted 22 days ago

Job description

Dyna Robotics trains robots to do real world manipulation tasks, and every one of those behaviors is learned from precisely labeled robot episodes. Our Data Annotation team turns raw teleoperation footage into the labels our imitation learning models depend on, so throughput and quality here are a direct input to how fast the company ships.
Dyna Robotics has raised over $140M, backed by top investors including CRV, First Round Capital, Robostrategy, Salesforce Ventures, NVentures, Amazon, Samsung Next, and LG Technology Ventures. Our team brings together engineers and researchers from Google, Meta, Apple, Amazon, Cruise, Aurora, NVIDIA, along with academic roots at Stanford, Berkeley, MIT, UPenn, and beyond. We're positioned to redefine the landscape of robotic automation.
The Role
This role owns the operational layer of that work. You will run the operation from day one, covering process, reporting, and third party relationships, and step into people management progressively, starting with a small pod of labelers and growing into the full team as you prove yourself. Our Annotation Lead keeps the technical judgment calls: model calibration and promotion, ontology and failure taxonomy design, and cost and planning strategy. You are the person who makes the annotation engine run predictably every single day.
What You'll Do
Operations and reporting
  • Own the reporting rhythm. Run daily and weekly throughput reporting across all active datasets, covering episode totals, review stage progress, and ETAs, and distribute scorecards to the labeling team and stakeholders.
  • Keep the pipeline moving. Own the Encord pipeline end to end: create and configure projects, set up SOPs and ontologies, move datasets through annotate, review, and complete stages, handle grade splits, assignments, and resyncs, and keep dataset updates flowing as new footage lands.
  • Measure and unblock. Track and continuously improve operational metrics such as throughput per headcount, per dataset cycle times, and expected versus actual labeling time, and flag bottlenecks before they turn into blockers.
  • Automate the mechanical. Extend and maintain the automation and runbooks behind these tasks, like the scripted daily totals send, so manual toil shrinks over time.

People and team management
  • Start with a pod, then scale up. You will begin by leading a small pod of labelers, owning their day to day assignments and first line supervision. As that proves out, you will transition into managing the full annotation team, about 12 people today and growing, and formalize sub leads as it scales.
  • Handle approvals. Own timecard, payroll hours, and approval workflows for the team.
  • Grow the team. Run hiring for the annotation team end to end, including sourcing, interviewing, onboarding, and ramp.
  • Develop people. Own performance management: regular 1:1s, performance reviews, coaching, and quality feedback loops.

Third party and vendor management
  • Own the relationships. Be the primary point of contact for our annotation platform (Encord) and for external labeling vendors and partners.
  • Coordinate partners. Run external workstreams by provisioning access and invites, standing up projects and SOPs for partners, and managing scope and priorities with them.
  • Manage the handoffs. Own the scale and vendor upload runbook, and make sure data moves cleanly between our systems and external tooling.
  • Absorb the overhead. Represent annotation ops in vendor and external syncs so the rest of the team stays focused.

What You'll Bring
  • 3+ years running operations, program management, or team management, ideally in data annotation, data operations, ML data pipelines, BPO or vendor management, or a comparable high throughput environment.
  • Direct people management experience. You have supervised a team, run hiring, and handled performance and approvals.
  • Strong operational instincts. You build repeatable processes, track the right metrics, and are comfortable owning dashboards, scorecards, and reporting.
  • Comfort with annotation and labeling tooling such as Encord, plus enough technical fluency to work in spreadsheets, basic SQL or scripts, and pipeline tools without hand holding.
  • Vendor and third party management experience. You can be the accountable point of contact and keep external partners on scope and on schedule.
  • A high tolerance for fragmentation. You can hold roughly 20 small threads a week and still keep the operation predictable.
  • Clear, proactive communication. You surface risks early and keep stakeholders aligned.
Bonus points for
  • Exposure to robotics, computer vision, or imitation learning data.
  • Experience automating manual ops workflows with scripts or lightweight tooling.
  • Experience scaling an annotation team through a growth phase and standing up sub leads.

At Dyna Robotics, we build technology for the real world, which requires a team as diverse as the environments our robots inhabit. We are an equal opportunity employer committed to technical rigor and mutual respect.
Don't let a checklist stop you. Data shows that underrepresented groups often only apply if they meet 100% of the criteria. We value problem-solving and grit over keyword matching. If you're passionate about closing the loop between deployed robots and better models, we want to hear from you, even if you don't check every box.

Apply