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Remote Amazon Data Annotation Jobs in Washington, DC

Eliassen Group

ETL Data Engineer

Tysons, VA · Remote

$70 - $88/hr

Description: Hybrid 3 days onsite / 2 days remote in Mclean, VA Our client seeks an ETL Data ... Experience with foundation model APIs such as Anthropic Claude, Amazon Nova, or OpenAI.

GrammaTech, Inc.

Software Engineer III

Arlington, VA · Remote

$165K - $190K/yr

Location: This is a remote opportunity Clearance: Active Secret Clearance Preferred Key ... Provide technical leadership for the development of new and enhanced data source integrations.

New

BlackSky

Staff Engineer, AI

Herndon, VA · On-site +1

Collaborate with the Data QA team to define annotation standards, resolve taxonomy issues, and ... Experience working with remote sensing imagery including geometry, radiometric normalization ...

Kobie Marketing

AI Engineer

Annapolis, MD · Remote

Join a National Top Workplace Named a Top Workplace in the USA and Top Remote Workplace, Kobie is ... on Amazon AgentCore that automates analyst workflows, surfaces insights from program data in ...

Kobie Marketing

AI Engineer

Annapolis, MD · Remote

Join a National Top Workplace Named a Top Workplace in the USA and Top Remote Workplace, Kobie is ... on Amazon AgentCore that automates analyst workflows, surfaces insights from program data in ...

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All JobsRemote Amazon Data Annotation JobsRemote Amazon Data Annotation Jobs in Washington, DC

Remote Amazon Data Annotation information

What is the difference between Remote Amazon Data Annotation vs Remote Mechanical Turk Worker?

AspectRemote Amazon Data AnnotationRemote Mechanical Turk Worker
CredentialsNo formal certifications required, but attention to detail helpsNo formal certifications required, basic task understanding needed
Work EnvironmentRemote, flexible hours, online platformRemote, flexible hours, online micro-task platform
Employer & IndustryAmazon, e-commerce, AI training dataVarious clients, data labeling, surveys, research

Remote Amazon Data Annotation involves labeling data specifically for Amazon's AI and e-commerce needs, often requiring attention to detail. Mechanical Turk workers perform a variety of micro-tasks across industries. While both are remote and flexible, data annotation is more specialized for AI training, whereas Mechanical Turk offers broader task types.

What are some common challenges faced by Remote Amazon Data Annotation specialists and how can they be addressed?

Remote Amazon Data Annotation specialists often encounter challenges such as maintaining consistency and accuracy across large volumes of data, managing repetitive tasks, and staying engaged while working independently. To address these, it's important to develop a strong attention to detail, utilize quality control tools provided by the platform, and take regular breaks to minimize fatigue. Additionally, staying connected with your team through regular check-ins and feedback sessions can help ensure alignment on annotation guidelines and improve overall performance.

What are Remote Amazon Data Annotation jobs?

Remote Amazon Data Annotation jobs involve labeling, categorizing, or tagging data such as images, text, or audio to help train machine learning models used by Amazon. Employees work from home using specialized tools to ensure accuracy and consistency in the data provided. These roles often require attention to detail, the ability to follow guidelines, and sometimes specific domain knowledge depending on the project. Data annotation is essential for improving the performance of AI systems in tasks like product recommendations, voice recognition, and search algorithms. These roles may be full-time, part-time, or project-based, offering flexibility for remote workers.

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

To thrive as a Remote Amazon Data Annotation Specialist, you need strong attention to detail, analytical thinking, and familiarity with data labeling concepts, often supported by a high school diploma or relevant experience. Competence with web-based annotation tools, cloud-based platforms, and sometimes Amazon-specific data systems is typically required. Diligence, consistency, effective communication, and the ability to work independently are valuable soft skills in this role. These skills and qualities are important to ensure high-quality, accurate data labeling that supports effective machine learning and AI model development.
What are the most commonly searched types of Amazon Data Annotation jobs in Washington, DC? The most popular types of Amazon Data Annotation jobs in Washington, DC are:
What are popular job titles related to Remote Amazon Data Annotation jobs in Washington, DC? For Remote Amazon Data Annotation jobs in Washington, DC, the most frequently searched job titles are:
What job categories do people searching Remote Amazon Data Annotation jobs in Washington, DC look for? The top searched job categories for Remote Amazon Data Annotation jobs in Washington, DC are:
Remote Amazon Data Annotation jobs near you
GenAI Data Automation Engineer

GenAI Data Automation Engineer

System One

Washington, DC • Remote

$48.95/hr

Contractor

Medical, Dental, Vision, Life, Retirement

Posted 2 days ago

Job description

Job Title: GenAI Data Automation Engineer Location: Washington, DC (Remote) Type: Contract To Hire Compensation: $48.95 /hr Security Clearance: Public Trust Responsibilities

  • Design and maintain data pipelines in AWS using S3, RDS/SQL Server, Glue, Lambda, EMR, DynamoDB, and Step Functions.
  • Develop ETL/ELT processes to move data between DynamoDB, SQL Server (AWS), and Azure SQL systems.
  • Integrate AWS Connect CRM data into the enterprise data pipeline for analytics and operational reporting.
  • Engineer, enhance ingestion pipelines with Apache Spark, Flume, Kafka for real-time and batch processing into Apache Solr, AWS Open Search platforms.
  • Leverage Generative AI services and Frameworks (AWS Bedrock, Amazon Q, Azure OpenAI, Hugging Face, LangChain) to create automated processes for vector generation and embeddings, automate data quality checks, metadata tagging, and lineage tracking, and enhance ingestion/ETL with LLM-assisted transformation and anomaly detection.
  • Build conversational BI interfaces that allow natural language access to Solr and SQL data.
  • Develop AI-powered copilots for pipeline monitoring and automated troubleshooting.
  • Implement SQL Server stored procedures, indexing, query optimization, profiling, and execution plan tuning to maximize performance.
  • Apply CI/CD best practices using GitHub, Jenkins, or Azure DevOps for data pipelines and GenAI model integration.
  • Ensure security and compliance through IAM, KMS encryption, VPC isolation, RBAC, and firewalls.
  • Support Agile DevOps processes with sprint-based delivery of pipeline and AI-enabled features.
Requirements:
  • BS in Computer Science or related field with 2+ years of data engineering, automation experiences.
  • Hands-on experience with SQL, SSIS, Python, Spark, Bash, PowerShell, AWS/Azure CLIs.
  • Experience with AWS services like S3, RDS/SQL Server, Glue, Lambda, EMR, DynamoDB.
  • Familiarity with Apache Flume, Kafka, Solr for large-scale data ingestion and search.
  • Familiarity with LLM, Gen AI frameworks using AWS Bedrock, Azure OpenAI or open source platforms and tools.
  • Experience with integrating REST API calls in workflows.
  • Familiarity with JIRA, GitHub / Azure DevOps / Jenkins for SDLC and CI/CD automation.
  • Strong troubleshooting and performance optimization skills in SQL, Spark or other data solutions.
  • Experience operationalizing Generative AI (GenAI Ops) pipelines, including model deployment, monitoring, retraining, and lifecycle management for LLMs and AI-enabled data workflows.
  • Good communication and presentation skills.
  • Ability to obtain Federal government Public Trust clearance.
Preferred (plus)
  • Certifications: AWS Data Engineer, AWS AI/ML Specialty, Azure AI Engineer, Databricks Data Engineer.
  • Experience implementing RAG pipelines, embeddings, and vector search with Solr, OpenSearch, FAISS, Pinecone, or Pgvector/SQL server vector types.
  • Experience with GenAI powered coding tools such as Claude Code, OpenAI Codex, VS Code.
  • Experience with multi-cloud data integration (AWS – Azure SQL).
  • Familiarity with Microsoft BizTalk and SSIS for SQL Server ETL workflows.
  • Knowledge of data lineage/governance tools (Purview, Unity Catalog, AWS Glue Catalog).
  • Familiarity with Infrastructure-as-Code (Terraform/CloudFormation, Bicep) for deployments.
  • Experience with compliance frameworks (FedRAMP, PCI-DSS, HIPAA).

System One, and its subsidiaries including Joulé and Mountain Ltd., are leaders in delivering outsourced services and workforce solutions across North America. We help clients get work done more efficiently and economically, without compromising quality. System One not only serves as a valued partner for our clients, but we offer eligible employees health and welfare benefits coverage options including medical, dental, vision, spending accounts, life insurance, voluntary plans, as well as participation in a 401(k) plan.

System One is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, age, national origin, disability, family care or medical leave status, genetic information, veteran status, marital status, or any other characteristic protected by applicable federal, state, or local law.

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