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Ai Tagging Jobs (NOW HIRING)

Influur

AI Engineer

New York, NY · On-site +1

$150K - $200K/yr

Past experience in video content classification/embeddings/tagging. You're the Type Who * Builds like an AI native, automates, experiments, and ships at lightning speed. * Brings a generalist mindset ...

Influur

AI Engineer

San Francisco, CA

$150K - $200K/yr

Past experience in video content classification/embeddings/tagging. You're the Type Who * Builds like an AI native, automates, experiments, and ships at lightning speed. * Brings a generalist mindset ...

Influur

AI Engineer

New York, NY

$150K - $200K/yr

Past experience in video content classification/embeddings/tagging. You're the Type Who * Builds like an AI native, automates, experiments, and ships at lightning speed. * Brings a generalist mindset ...

Influur

AI Engineer

San Francisco, CA · On-site +1

$150K - $200K/yr

Past experience in video content classification/embeddings/tagging. You're the Type Who * Builds like an AI native, automates, experiments, and ships at lightning speed. * Brings a generalist mindset ...

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Ai Tagging information

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$39K

$114.3K

$150K

How much do ai tagging jobs pay per year?

As of Jul 24, 2026, the average yearly pay for ai tagging in the United States is $114,320.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,500.00 and $134,500.00 per year, depending on experience, location, and employer.

What does a typical day look like for someone in an AI Tagging role?

A typical day for an AI Tagging professional includes reviewing and labeling large sets of data—such as images, audio, or text—according to specific guidelines provided by the project. You may spend time collaborating with data scientists or project managers to clarify labeling instructions or resolve ambiguous cases. Work is usually structured with clear quality and productivity targets, and you might also participate in feedback sessions to improve annotation consistency. The pace can be steady, with periods of high concentration, and you may use specialized software platforms to manage your workflow. Team communication and attention to detail are key aspects of the job each day.

What are the key skills and qualifications needed to thrive in the Ai Tagging position, and why are they important?

To thrive in AI Tagging, you need strong attention to detail, data annotation skills, and familiarity with data quality standards, often backed by experience or coursework in information science or computer science. Familiarity with data labeling platforms, image and text annotation tools, and occasionally basic programming or scripting knowledge is beneficial. Strong organizational skills, patience, and the ability to work efficiently both independently and within a team distinguish top performers in this role. These skills ensure the accurate and efficient creation of high-quality labeled datasets that are essential for training and improving AI models.

What is an AI Tagging job?

An AI Tagging job involves labeling or annotating data to help train machine learning models. This can include tagging images, videos, text, or audio with relevant metadata so that AI systems can recognize patterns and improve accuracy. AI taggers follow specific guidelines to ensure consistency and quality in the annotations. It's a crucial step in developing AI systems for tasks like image recognition, natural language processing, and recommendation algorithms.

More about Ai Tagging jobs
What cities are hiring for Ai Tagging jobs? Cities with the most Ai Tagging job openings:
What are the most commonly searched types of Ai Tagging jobs? The most popular types of Ai Tagging jobs are:
What states have the most Ai Tagging jobs? States with the most job openings for Ai Tagging jobs include:
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Ai Tagging jobs near you
Infographic showing various Ai Tagging job openings in the United States as of July 2026, with employment types broken down into 73% Full Time, 24% Part Time, and 3% Contract. Highlights an 65% Physical, 3% Hybrid, and 32% Remote job distribution, with an average salary of $114,320 per year, or $55 per hour.

Senior RF Machine Learning Engineer

Quartermaster AI Inc

Arlington, VA • On-site

$210K - $260K/yr

Full-time

Posted 7 days ago

Job description

About Us:
At Quartermaster AI, we believe the ocean should be a safe and sustainably managed resource for all. By leveraging cutting-edge AI and robotics, we unlock capabilities that were only recently impossible. Our distributed open-ocean systems enable every vessel to sense, compute, and communicate, enhancing maritime domain awareness for those who need it most.
Job Description:
Quartermaster AI is seeking a Senior AI/ML Engineer with an emphasis in RF analysis to develop and deploy machine learning systems that utilize RF data for real-time maritime intelligence.
You'll work in a small team of experienced engineers to build detection, classification, and tagging models that help provide contextual understanding of vessel activity based on observed RF signatures.
Key Responsibilities:
  • Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking.
  • Build and maintain dataset curation pipelines, including AIS-correlated ground truth labeling, synthetic RF data generation, and augmentation strategies for class-imbalanced maritime environments.
  • Build the interface between DSP feature outputs and model inputs by defining pre-processing, normalization, and feature extraction requirements in coordination with the DSP engineer.
  • Develop model evaluation frameworks and benchmarking harnesses; define quantitative performance criteria and drive iterative improvement against them.
  • Optimize models and inference workflows for deployment on edge compute hardware.
  • Document model architecture, training methodology, dataset provenance, and validation results.
Qualifications (Preferred):
  • Master's or PhD in Machine Learning, Signal Processing, or a closely related field - or equivalent demonstrated experience.
  • 5+ years building and deploying ML systems with a focus on RF or signals data.
  • Proficiency in Python and deep learning frameworks; familiarity with RF-native tooling such as Torchsig is a strong plus.
  • Strong understanding of signal alignment, temporal synchronization, and feature extraction from IQ and spectral data.
  • Proven ability to ship production models, not just research prototypes.
  • Experience in maritime, aerospace, or operationally demanding spectral environments.
  • Experience building labeled RF datasets from ground truth sources.
  • Familiarity with edge inference constraints and optimization techniques (quantization, pruning, model distillation).
  • Active Secret clearance or demonstrated ability to obtain one.

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