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Asynchronous Ai Writing Evaluator Jobs in Riverside, CA

Anduril Industries

AI Systems Engineer

Santa Ana, CA · On-site

This is not a prompt-writing role. It is a systems-engineering role for someone who has already delivered production AI systems, understands evaluation and rollback, and knows how to make ...

New

Anduril Industries

AI Systems Engineer

Santa Ana, CA · On-site

$191K - $253K/yr

This is not a prompt-writing role. It is a systems-engineering role for someone who has already delivered production AI systems, understands evaluation and rollback, and knows how to make ...

New

Melissa

AI Visibility Manager

Rancho Santa Margarita, CA · On-site

$80K - $120K/yr

The job is to make Melissa easier for AI answer engines and technical evaluators to resolve ... Excellent technical writing and editing skills, with the ability to turn complex capabilities into ...

Hyundai Capital

Sr. AI Agent / Prompt Engineer

Irvine, CA · On-site

$120 - $150/hr

This role goes beyond prompt writing and owns agent behavior end to end, including prompt systems ... Evaluation, Quality, and Reliability * Define and run offline and online evaluations for agent ...

Hyundai Capital

Sr. AI Agent / Prompt Engineer

Irvine, CA · On-site

$132K - $204K/yr

This role goes beyond prompt writing and owns agent behavior end to end, including prompt systems ... evaluation, and reliability in real business workflows. This role will operate independently ...

Veritone

AI/Machine Learning Engineer

Irvine, CA · On-site

Proven track record of writing maintainable code, including unit/integration tests, evaluation ... AI/ML & Data: Python, PyTorch / TensorFlow, Hugging Face, LangChain / LlamaIndex, Vector DBs (e.g ...

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Asynchronous Ai Writing Evaluator information

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$30.8K

$68.3K

$111.1K

How much do asynchronous ai writing evaluator jobs pay per year?

As of Aug 30, 2026, the average yearly pay for asynchronous ai writing evaluator in Riverside, CA is $68,304.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,400.00 and $82,900.00 per year, depending on experience, location, and employer.

What is an asynchronous AI writing evaluator?

Asynchronous AI Writing Evaluators are professionals who assess and review written content generated by artificial intelligence systems, such as essays or articles, without needing to do so in real-time. They typically work remotely, providing feedback on grammar, coherence, relevance, and overall writing quality to help improve AI models or ensure that AI-generated content meets certain standards. Their evaluations contribute to refining AI writing tools, making them more accurate and reliable for end users. Asynchronous evaluators work on their own schedules, reviewing assignments as they are available rather than during live sessions.

What are the key skills and qualifications needed to thrive as an asynchronous AI writing evaluator?

To thrive as an Asynchronous AI Writing Evaluator, you need a strong command of written language, critical analysis, and attention to detail, usually supported by a background in English, linguistics, or education. Familiarity with digital evaluation platforms, AI annotation tools, and proficiency in using online collaboration systems are typically required. Excellent time management, clear communication, and adaptability to evolving guidelines make someone stand out in this position. These skills are essential to ensure accurate, consistent, and constructive feedback that improves AI writing models and supports quality assurance.

What are the most common challenges faced by an asynchronous AI writing evaluator, and how can they be managed effectively?

As an Asynchronous AI Writing Evaluator, one of the main challenges is maintaining consistency and objectivity across large volumes of varied writing samples, often reviewed independently without real-time peer interaction. Managing time efficiently and developing a clear understanding of evaluation rubrics are essential for providing accurate and constructive feedback. Regular communication with team leads and participating in calibration sessions can help align expectations and address ambiguities, ensuring high-quality evaluations. Additionally, being proactive about seeking clarification and utilizing available support resources can greatly enhance both accuracy and job satisfaction.

What is the difference between Asynchronous Ai Writing Evaluator vs Content Moderator?

AspectAsynchronous Ai Writing EvaluatorContent Moderator
Required CredentialsTypically requires a degree in English, Communications, or related field; familiarity with AI toolsOften requires high school diploma or equivalent; training in content policies
Work EnvironmentRemote, independent work analyzing AI-generated writingRemote or on-site, reviewing user-generated content for compliance
Employer & Industry UsageUsed by educational platforms, AI companies, and e-learning providersEmployed by social media, online forums, and content-sharing platforms

While both roles involve reviewing digital content, the Asynchronous Ai Writing Evaluator focuses on assessing AI-generated writing for quality and accuracy, often working independently and with AI tools. Content Moderators primarily review user-generated content to ensure compliance with community standards. Both roles require attention to detail but serve different purposes within the digital content ecosystem.

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Senior Generative AI Developer - Google Cloud

Irvine, CA • On-site

Jobtailor
See more jobs at Jobtailorexternal

$140 - $210/hr

Other

Posted 12 days ago

Job description

  • Design, build, test, and deploy Generative AI applications and intelligent agents on Google Cloud.
  • Develop single-agent and multi-agent solutions using Google Agent Development Kit.
  • Integrate Gemini models with enterprise APIs, databases, applications, and business workflows.
  • Deploy AI applications using Agent Engine, Cloud Run, GKE, or other appropriate GCP services.
  • Build Retrieval-Augmented Generation solutions using services such as BigQuery, Vertex AI Vector Search, Cloud Storage, and Document AI.
  • Develop APIs, microservices, agent tools, MCP integrations, and event-driven workflows.
  • Build data pipelines to ingest, transform, chunk, embed, index, and retrieve structured and unstructured data.
  • Implement session management, memory, tool calling, human approval, and agent orchestration patterns.
  • Apply automated testing, CI/CD, logging, monitoring, tracing, evaluation, and cost-management practices.
  • Implement Google Cloud security using IAM, service accounts, Workload Identity Federation, Secret Manager, and private networking.
  • Troubleshoot issues across agents, models, APIs, data pipelines, integrations, security, and cloud deployments.
  • Create architecture diagrams, technical designs, API specifications, deployment guides, and operational documentation.
  • Own technical workstreams and provide design reviews, code reviews, and guidance to other developers.
  • Participate in client discovery, architecture, testing, deployment, and knowledge-transfer activities.
Requirements
  • Significant experience developing and deploying applications on Google Cloud.
  • Advanced Python development experience.
  • Hands-on experience building Generative AI or agentic applications.
  • Experience with Google Agent Development Kit, including agents, tools, workflows, sessions, state, and multi-agent patterns.
  • Experience integrating Gemini models using Vertex AI or Google Gen AI SDKs.
  • Experience with Agent Engine, Cloud Run, GKE, Cloud Functions, or similar GCP runtimes.
  • Experience designing and implementing RAG solutions.
  • Experience with BigQuery and Google Cloud data services.
  • Experience building APIs using frameworks such as FastAPI.
  • Experience with REST APIs, asynchronous processing, event-driven architecture, and microservices.
  • Understanding of MCP and its use in connecting agents to enterprise tools and systems.
  • Experience with SQL, document stores, object storage, embeddings, semantic search, or vector databases.
  • Experience with Git, automated testing, CI/CD, Docker, and infrastructure as code.
  • Understanding of Google Cloud IAM, service accounts, Secret Manager, networking, logging, and monitoring.
  • Ability to evaluate tradeoffs involving model quality, latency, security, scalability, reliability, and cost.
Core Competencies

Demonstrates expertise in developing and deploying Generative AI applications on Google Cloud, utilizing advanced Python programming and integrating various Google Cloud services. Proficient in building APIs, data pipelines, and implementing security measures while ensuring high-quality, scalable, and reliable solutions.

Highest-signal resume keywords
  • Google Cloud Application Development
  • Advanced Python Development
  • Generative AI Application Building
  • API Development with FastAPI
  • Data Pipeline Construction
ATS Optimization Keywords Hard Skills
  • Python
  • Google Cloud
  • Generative AI
  • API Development
  • BigQuery
  • SQL
  • Microservices
  • CI/CD
  • Docker
  • Event-Driven Architecture
Industry Keywords
  • Generative AI
  • Agentic Applications
  • MCP
  • Asynchronous Processing
  • Retrieval-Augmented Generation
  • Data Services
  • Cloud Security
  • Monitoring
  • Logging
  • Orchestration Patterns
Tools & Technologies
  • Google Agent Development Kit
  • Agent Engine
  • Cloud Run
  • GKE
  • Vertex AI
  • Cloud Functions
  • Document AI
  • Secret Manager
  • Git
  • Infrastructure as Code
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