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Remote Amazon Data Annotation Jobs in Cleveland, TX

AI Software Engineer

Houston, TX · Remote

$115K - $145K/yr

  • Medical

  • Retirement

  • PTO

AI frameworks -- Building agentic and LLM applications with tools like Amazon Bedrock, LangGraph ... remote sensing imagery, and libraries such as GDAL, rasterio, GeoPandas, or Shapely. * Data ...

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Remote Amazon Data Annotation information

What is a remote Amazon data annotation job?

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 skills and qualifications are needed for a remote Amazon data annotation specialist?

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 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 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 job categories do people searching Remote Amazon Data Annotation jobs in Cleveland, TX look for?

The top searched job categories for Remote Amazon Data Annotation jobs in Cleveland, TX are:

What cities near Cleveland, TX are hiring for Remote Amazon Data Annotation jobs?

Cities near Cleveland, TX with the most Remote Amazon Data Annotation job openings:

AI Software Engineer

Arva Intelligence

Houston, TX • Remote

$115K - $145K/yr

Full-time

Medical, Retirement, PTO

Posted 13 days ago


Job description

Reports to: Chief Technology Officer

Location: Remote

Level: 2-5 years of professional software development experience

Base Salary Range: $115K –145K

Position Summary

We are seeking an AI Software Engineer to design, build, and operate the AI-enabled services at the core of the Arva platform. This role sits at the intersection of applied AI engineering and production software development: you will build agent-based systems using frameworks such as LangGraph and LangChain, and you will also build the durable application and data infrastructure including APIs, pipelines, and interfaces that make those systems reliable at scale.

This is a hands-on engineering role for someone who has moved past their first job, ships production code independently, and is looking to apply modern AI techniques to a domain where correctness genuinely matters.

Key Responsibilities

  • Design, build, and maintain LLM-powered and agentic workflows using tools like LangGraph and LangChain, AWS Bedrock AgentCore, including tool use, retrieval, memory, orchestration, and multi-step reasoning pipelines.
  • Develop and productionize AI/ML services in Python, including work with Mixture-of-Experts (MoE) and other multi-model architectures for routing, specialization, and ensemble prediction.
  • Build and extend backend services and APIs in Django, with PostgreSQL (and PostGIS) as the primary data layer.
  • Implement front-end features and internal tooling in TypeScript and Vue.js to expose AI capabilities and analytics to users.
  • Build and optimize data processing pipelines that ingest, clean, normalize, and enrich large volumes of geospatial, agronomic, imagery, and third-party data.
  • Develop and maintain integrations with external partner and customer APIs, including authentication, rate limiting, schema mapping, error handling, and monitoring.
  • Deploy, scale, and operate services on AWS using managed compute, storage, database, queueing, and serverless offerings.
  • Design and execute QA/QC processes for both software and data — automated testing, validation rules, anomaly detection, model evaluation, and regression checks — so that model outputs meet the accuracy and auditability standards our customers and verification bodies require.
  • Participate in code review, architecture discussions, and technical planning; write clear documentation for the systems you build.
  • Partner with data scientists, agronomists, and product stakeholders to translate scientific and business requirements into production systems.

Required Qualifications

  • 2–5 years of professional software development experience building and shipping production systems.
  • Strong proficiency in Python, including experience with modern application frameworks, testing, and packaging practices.
  • Demonstrated experience building applications or services that use large language models or other machine learning models in production.
  • Working knowledge of relational databases and SQL, with practical experience designing schemas and writing performant queries.
  • Experience developing and consuming RESTful APIs.
  • Familiarity with cloud infrastructure (AWS preferred) and standard software delivery practices including version control, CI/CD, and automated testing.
  • Ability to work independently on ambiguous problems, and to communicate technical trade-offs clearly to both technical and non-technical colleagues.
  • Bachelor's degree in Computer Science, Software Engineering, a related technical field, or equivalent practical experience.

Preferred Qualifications

The ideal candidate will bring hands-on experience across several of the following. We do not expect any single candidate to have all of them.

  • AI frameworks — Building agentic and LLM applications with tools like Amazon Bedrock, LangGraph and LangChain.
  • Model architecture — Familiarity with Mixture-of-Experts (MoE) architectures, model routing, and ensemble or multi-model system design.
  • Backend — Production experience with Python and the Django framework.
  • Frontend — Proficiency with TypeScript, JavaScript, and Vue.js.
  • Data storage — Advanced PostgreSQL experience, including performance tuning and the PostGIS extension.
  • Geospatial — Experience with geospatial data formats, coordinate systems, raster and vector processing, satellite or remote sensing imagery, and libraries such as GDAL, rasterio, GeoPandas, or Shapely.
  • Data processing — Building large-scale ETL/ELT pipelines, batch and streaming workflows, and orchestration tooling.
  • AWS — Depth across services such as Lambda, S3, ECS/Fargate, RDS, Batch, SQS/SNS, Step Functions, and IAM.
  • Integrations — Designing and maintaining robust third-party and partner API integrations.
  • QA/QC — Establishing automated quality assurance and quality control processes for software, data, and model outputs.
  • Domain — Prior work in agriculture, agronomy, remote sensing, climate, energy, or environmental markets.

About Arva Intelligence

Arva is a machine learning software-based SaaS company with offices located in Houston, TX and Park City, UT. Arva's platform was built to apply our novel ML technology to the agricultural industry, optimizing and measuring regenerative practices, improving crop yields, and reducing operational costs for producers. Our platform helps our customers and partners capitalize on natural regenerative practices; by providing recommendations that improve environmental and ecological ecosystems. Platform features include practice verification and registration, as well as the sale of environmental asset credits to our corporate buyers. Thus, Arva is helping to keep the planet green by providing a green-tech platform that informs, measures, validates, predicts, and registers carbon exchange opportunities, allowing growers and ranchers to produce and sell credits that are bought by our corporate partners, who endorse sustainable food supply and carbon neutrality.

This job description reflects the core duties of the role but is not intended to be all-inclusive. The role may evolve as the company grows, requiring additional responsibilities or changes in scope.

Why you'll love working here:

  • Be part of building something innovative and foundational in regenerative agriculture and sustainability
  • Contribute to work that's bigger than any one role and drives lasting impact across the globe
  • Meaningful career growth as we scale
  • Target annual bonus
  • 401(k) contribution
  • Flexible PTO
  • Stock options
  • 100% employer-paid health insurance premiums 

Employment Eligibility 

Only applicants currently eligible to work in the United States will be considered for this position.