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Remote Amazon Computer Science Jobs in Ware Shoals, SC

Remote Amazon Computer Science information

What are remote Amazon computer science jobs?

Remote Amazon computer science jobs are roles at Amazon that allow professionals to work from locations outside of Amazon offices, focusing on computer science domains such as software development, data engineering, machine learning, and cloud computing. These positions typically require strong technical skills in programming, algorithms, and systems design, and they often support Amazon’s various products and services from a virtual setting. Remote roles offer flexibility while still enabling collaboration with global teams and contributing to large-scale projects. Candidates usually need a degree in computer science or a related field and relevant work experience.

How do remote Amazon Computer Science professionals typically collaborate with their teams across different time zones?

Remote Amazon Computer Science professionals often work with colleagues spread across various regions, which requires strong communication and time management skills. Teams commonly use tools like Amazon Chime, Slack, and project management platforms to coordinate meetings, share updates, and track progress asynchronously. Flexibility in scheduling and clear documentation are crucial to ensure everyone stays informed and aligned, and regular virtual stand-ups or check-ins help maintain team cohesion. Despite the remote setting, Amazon fosters a collaborative environment where engineers support each other's growth and problem-solving.

What are the key skills and qualifications needed to thrive as a Remote Amazon Computer Science professional, and why are they important?

To thrive as a Remote Amazon Computer Science professional, you typically need a strong background in computer science fundamentals, programming (such as Java, Python, or C++), and a relevant degree or equivalent experience. Familiarity with cloud computing platforms like AWS, software development tools, and version control systems such as Git is essential. Strong problem-solving, self-motivation, and effective communication are key soft skills for collaborating remotely and driving projects forward. These skills and qualities are critical for delivering scalable, high-quality solutions while working independently within Amazon's dynamic and distributed teams.

What is the difference between Remote Amazon Computer Science vs Remote Amazon Software Development?

AspectRemote Amazon Computer ScienceRemote Amazon Software Development
Required CredentialsBachelor's in Computer Science or related field, possibly some certificationsBachelor's in Computer Science or related field, often with coding certifications
Work EnvironmentResearch, data analysis, algorithm design, theoretical workCode writing, application development, system implementation
Employer & Industry UsageAmazon research labs, data teams, algorithm groupsAmazon software engineering teams, product development
Common Search & Comparison IntentUnderstanding research roles vs development roles at AmazonDistinguishing between development and research positions at Amazon

Remote Amazon Computer Science roles focus on research, algorithms, and theoretical work, while Remote Amazon Software Development roles emphasize coding, application building, and system implementation. Both require a strong foundation in computer science but differ in daily tasks and work environment.

Content Automation Specialist

1 point system

Fruit Hill, SC • Remote

Contractor

Posted 15 days ago


Job description

Requirement - Content Automation Specialist

Location- 100% Remote

Contract W2

Ex- Microsoft

Must Haves:
Python
Testing Framework
Some AI knowledge
Job Description:
Overview
We are seeking a highly technical and strategic Content Automation Specialist to transform how content quality is validated across Microsoft 365 and Copilot experiences.
This role focuses on designing scalable evaluation systems, automated testing workflows, and AI-assisted quality frameworks that increase efficiency while maintaining human-centered quality standards. The ideal candidate understands where automation can replace manual review, where human evaluation remains critical, and how to build systems that continuously improve content quality at scale.
This individual will partner with cross-functional teams to define testing strategies, build evaluation frameworks, develop automation opportunities, and shape the future of content quality operations.
Responsibilities
Quality Systems Strategy
• Design scalable testing and evaluation frameworks for content, templates, prompt templates, and AI-generated experiences.
• Define quality measurement approaches and establish testing standards across content ecosystems.
• Create recommendations for balancing automation, AI evaluation, and human review.
Automation & Tooling
• Develop and implement automated testing workflows and validation systems.
• Build tools and processes that reduce manual effort while maintaining content quality.
• Identify opportunities to leverage AI for defect detection, content validation, and quality assurance.
Human Evaluation & AI Quality
• Design human-in-the-loop evaluation frameworks for assessing AI outputs.
• Establish quality rubrics, benchmarking criteria, and testing methodologies.
• Determine where human judgment adds measurable value and where automation can scale operations.
Data & Optimization
• Analyze testing metrics and operational data to identify improvement opportunities.
• Develop recommendations that improve quality, efficiency, and testing coverage.
• Measure the effectiveness of testing systems and continuously optimize workflows.
Cross-Functional Leadership
• Partner with cross-functional teams on automation recommendations and evaluations of quality risks.
• Drive adoption of testing standards and scalable evaluation practices.
• Influence long-term strategy for content quality and AI evaluation programs.
Required Qualifications
• Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, AI, or related field.
• 2-4 years of experience in test automation, quality engineering, AI evaluation, or systems development.
• Strong experience with Python and automation tooling.
• Experience with Copilot, VSCode, and other AI tooling and infrastructure.
• Experience building testing frameworks, quality systems, or workflow automation solutions.
• Experience interpreting data and translating findings into strategic recommendations.
• Strong technical problem-solving and systems-thinking skills.
Preferred Qualifications
• Experience evaluating generative AI systems or LLM-powered products.
• Experience designing human evaluation programs or human-in-the-loop workflows.
• Familiarity with content quality measurement and content operations.
• Experience working with machine learning, AI quality, or model evaluation frameworks.
• Understanding of Microsoft 365, Copilot, or productivity software ecosystems.
• Experience building internal tools, dashboards, or quality monitoring systems.