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Full Time Ai Tagging Jobs (NOW HIRING)

OPPO US Research Center

Senior AI Data Engineer

Palo Alto, CA · On-site

$134K - $161K/yr

Data Tagging & Profile System (User 360) * Establish a comprehensive User Tagging/Labeling System ... The US base salary range for this full-time position is $100,000-$300,000 + bonus + long term ...

Next League, LLC

AI Solutions Engineer

New York, NY · On-site

$200/hr

... full-time conversion introduces a comprehensive total rewards package, including premium health ... Computer vision for content operations (highlight detection, automated tagging, content moderation ...

QualX Corporation

AI Developer

Springfield, VA · On-site

... Job Type Full-Time Career Level Staff Education Bachelor's Degree Travel Security Clearance ... review, metadata tagging, digitization workflows, and compliance with DoD and Federal records ...

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Full Time Ai Tagging information

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How much do full time ai tagging jobs pay per year?

As of Jun 14, 2026, the average yearly pay for full time ai tagging in the United States is $78,135.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,500.00 and $88,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Full-Time AI Tagging Specialist, and why are they important?

To thrive as a Full-Time AI Tagging Specialist, you need strong attention to detail, data annotation skills, and familiarity with basic computer operations, often supported by a relevant degree or prior experience in data labeling. Proficiency with annotation tools such as Labelbox, Supervisely, or CVAT, and knowledge of data management systems, are typically required. Diligence, consistency, and effective communication help ensure high-quality, accurate labeling and smooth collaboration with development teams. These skills are crucial because precise data tagging directly influences the performance and reliability of AI models.

What are some common challenges faced by professionals in a full-time AI tagging role, and how can they be managed?

Professionals in full-time AI tagging roles often encounter challenges such as repetitive tasks, maintaining high accuracy under tight deadlines, and adapting to evolving data labeling guidelines. Staying attentive to detail and following clear documentation can help mitigate errors, while using productivity tools and taking regular breaks can combat fatigue. Regular communication with team leads or quality assurance specialists is also crucial to clarify ambiguities and ensure consistent tagging standards.

What is a Full Time AI Tagging job?

A Full Time AI Tagging job involves labeling or annotating data—such as images, text, or audio—so that artificial intelligence (AI) systems can learn from it. This work is essential in training machine learning models to recognize patterns and make accurate predictions. Taggers follow specific guidelines to ensure consistency and accuracy in the data, often using specialized software tools. The role may also include reviewing or quality-checking tags created by others, making it a critical part of the AI development process.
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What cities are hiring for Full Time Ai Tagging jobs? Cities with the most Full Time Ai Tagging job openings:
What are the most commonly searched types of Ai Tagging jobs? The most popular types of Ai Tagging jobs are:
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Full Time Ai Tagging jobs near you
Infographic showing various Full Time Ai Tagging job openings in the United States as of June 2026, with employment types broken down into 100% Part Time. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $78,135 per year, or $37.6 per hour.

Senior AI Data Engineer

OPPO US Research Center

Palo Alto, CA • On-site

$134K - $161K/yr

Full-time

Posted 19 days ago

Job description

We are seeking a forward-thinking AI Data Engineer to bridge the gap between our user data assets and advanced AI capabilities. In this role, you will be the architect of our user data foundation, building a robust data warehouse and a dynamic tagging system. Crucially, you will leverage this data to integrate with third-party Large Language Models (LLMs), enabling intelligent, data-driven interactions and next-generation user experiences.

Key Responsibilities

  • User Data Warehouse Construction & Architecture
  1. Design, build, and maintain a scalable User Data Warehouse to consolidate data from fragmented sources.
  2. Design efficient data models to support high-performance querying and analytics.
  3. Implement ETL/ELT pipelines to ensure real-time or near-real-time data availability and quality.
  • Data Tagging & Profile System (User 360)
  1. Establish a comprehensive User Tagging/Labeling System (User Portrait).
  2. Develop algorithms to generate static, behavioral, and predictive tags to accurately segment users.
  3. Ensure the tagging system is dynamic and can update in real-time to reflect the latest user interactions.
  • LLM Integration & Data Intelligence
  1. Lead the integration of Large Language Models with our internal data.
  2. Design and implement RAG (Retrieval-Augmented Generation) pipelines to feed structured user profile data and tags into LLMs for personalized outputs.
  • Intelligent Interaction Development
  1. Develop APIs and middleware that allow downstream applications to interact with data using natural language.
  2. Optimize the "Data-to-AI" loop: ensure the LLM understands the context of the user data to provide accurate, hallucination-free responses.
  3. Monitor token usage, latency, and response quality of the AI interactions.

Requirements

  • Education: Master's degree in Computer Science, Data Engineering, Artificial Intelligence, or a related field.
  • Experience: 3-5+ years of experience in Data Engineering or Backend Development with a focus on data.
  • Data Stack:
  1. Proficiency in SQL and Python/Java/Scala.
  2. Hands-on experience with Data Warehouses (e.g. Snowflake, BigQuery, ClickHouse) and Big Data frameworks (Spark, Flink).
  3. Familiar with message middleware (Kafka) and containerization (Docker).
  4. User Data Experience: Proven experience in building CDP (Customer Data Platform), DMP, or User Profile/Tagging systems.
  • AI/LLM Skills:
  1. Experience interacting with LLM APIs (OpenAI, etc.) and inference optimization (vLLM).
  2. Familiarity with frameworks like LangChain, LlamaIndex, or Haystack.
  3. Understanding of Embedding, vector databases (FAISS, Milvus), and RAG architecture.
  • Soft Skills: Strong problem-solving abilities and the ability to translate business needs into technical data requirements.

Preferred Skills (Nice to Haves)

  • Experience with Prompt Engineering and optimizing context windows for efficient data feeding.
  • Knowledge of Knowledge Graphs (Neo4j, NebulaGraph) and how to combine them with LLMs.
  • Experience in model fine-tuning (SFT, RLHF).
  • Familiarity with privacy regulations (GDPR/CCPA) regarding user data and AI.
  • Experience with mature launched projects serving a large user base on cloud platforms (AWS, etc.).

Benefits

OPPO is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.

The US base salary range for this full-time position is $100,000-$300,000 + bonus + long term incentives benefits. Our salary ranges are determined by role, level, and location.

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