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Temporary Ai Trainer Jobs (NOW HIRING)

KēSTA I.T.

AI Engineer

Hartford, CT · On-site +1

$1/hr

... · Training reimbursement About KēSTA I.T.: Our name says it all; KēSTA I.T. (Keys-to-I.T.) AND ... We specialize in temporary and permanent placement of Software, Hardware, Network, Cloud, CRM/ERP, ...

Shield AI

Staff Facility Security Officer

Washington, DC · On-site

$100K - $150K/yr

Shield AI is a venture-backed defense-tech company with the mission of protecting service members ... Conduct security education, annual refresher training, and insider threat awareness training for ...

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How much do temporary ai trainer jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for temporary ai trainer in the United States is $24.74, according to ZipRecruiter salary data. Most workers in this role earn between $18.75 and $26.44 per hour, depending on experience, location, and employer.

What are some common challenges faced by temporary AI trainers, and how can they be addressed?

Temporary AI Trainers often face the challenge of quickly adapting to new data sets and annotation guidelines, as projects and requirements can change frequently. Balancing speed and accuracy is crucial, especially when labeling large volumes of data under tight deadlines. Communication with permanent team members is important to clarify uncertainties and ensure consistency in training data. To address these challenges, it's helpful to ask questions early, make use of available documentation, and participate actively in feedback sessions to continuously improve your work.

What is a temporary AI trainer?

Temporary AI Trainers are professionals hired on a short-term basis to help train artificial intelligence systems. Their responsibilities typically include labeling data, reviewing machine learning outputs, and providing feedback to improve AI models’ accuracy. These roles are essential for teaching AI systems to recognize patterns, understand language, or perform specific tasks. Temporary AI Trainers often work on projects with a defined duration or until a particular dataset is fully annotated. This position is ideal for those interested in the growing field of AI and machine learning but looking for flexible or project-based work.

What is the difference between Temporary Ai Trainer vs Data Annotator?

AspectTemporary Ai TrainerData Annotator
Required CredentialsBasic technical skills, familiarity with AI conceptsAttention to detail, basic computer skills
Work EnvironmentRemote or on-site, collaborative with AI teamsRemote or on-site, focused on data labeling tasks
Employer & Industry UsageTech companies, AI startups, research labsTech firms, data companies, AI development projects
Search & Comparison IntentUnderstanding roles in AI trainingData labeling and annotation roles

Temporary Ai Trainers and Data Annotators both support AI development but differ mainly in scope. Temporary Ai Trainers focus on training AI models through supervised tasks, requiring some technical knowledge. Data Annotators primarily label data to prepare datasets, emphasizing attention to detail. Both roles are essential in AI projects and often overlap in work environment and industry usage.

What are the key skills and qualifications needed to thrive as a temporary AI trainer?

To thrive as a Temporary AI Trainer, you need strong analytical skills, attention to detail, and a background in linguistics, data annotation, or a related field. Familiarity with annotation platforms, spreadsheet tools, and sometimes basic programming or scripting is often required. Excellent communication, adaptability, and the ability to follow detailed instructions make someone stand out in this position. These skills ensure high-quality data labeling and feedback, which are critical for improving AI models' accuracy and performance.

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Temporary Ai Trainer jobs near you
Infographic showing various Temporary Ai Trainer job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, and 4% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution, with an average salary of $51,453 per year, or $24.7 per hour.

Senior Staff Software Engineer, Perception (R4985)

Shield AI

Dallas, TX • On-site

$233K - $350K/yr

Full-time

Posted 19 days ago

Job description

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI's technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. 

The Hivemind Solutions Perception team develops the next generation of perception capabilities for autonomous systems by combining state-of-the-art machine learning with the proven foundations of computer vision. The team advances how autonomous platforms understand and interpret the world by developing vision, vision-language (VLM), and vision-language-action (VLA) models that tackle core perception challenges such as object understanding, scene interpretation, and mission-relevant environmental awareness. Working at the intersection of research and production, our engineers build the data pipelines, supervised fine-tuning (SFT) workflows, evaluation frameworks, and deployment infrastructure needed to transform cutting-edge AI research into reliable, mission-ready perception capabilities.
 
In this role, you'll develop and deploy advanced machine learning models that enable autonomous systems to better understand, reason about, and interact with their environment. You'll partner closely with machine learning researchers, autonomy engineers, perception engineers, and platform teams to translate emerging AI capabilities into reliable, production-ready systems for U.S. and international defense customers. This is an ideal opportunity for engineers who enjoy building state-of-the-art AI systems while solving the practical challenges of deploying them on operational autonomous platforms. 
What You'll Do:

Model Development - Design, train, fine-tune, and maintain state-of-the-art vision, vision-language, and vision-language-action models that improve perception and decision-making for autonomous systems. 

Data Pipelines & Model Training - Build scalable data pipelines, supervised fine-tuning (SFT) workflows, and evaluation loops that continuously improve model performance on mission-relevant tasks. 

Model Deployment & Optimization - Deploy and optimize machine learning models for embedded hardware using technologies such as ONNX, TensorRT, and hardware-accelerated inference frameworks. 

Perception & Autonomy Applications - Apply modern machine learning techniques to solve challenging perception and autonomy problems across aerial and other autonomous systems operating in complex, real-world environments. 

Research-to-Production - Translate cutting-edge machine learning research into production-ready capabilities by balancing model performance, robustness, computational efficiency, and operational reliability. 

Cross-functional Collaboration - Partner closely with perception, autonomy, platform, and software engineering teams to integrate machine learning capabilities into mission-ready autonomous systems. 

Model Evaluation & Validation - Develop benchmarks, testing methodologies, and evaluation frameworks to measure model performance, identify failure modes, and guide future improvements. 

Continuous Improvement - Improve training infrastructure, developer tooling, deployment workflows, and model lifecycle management to accelerate experimentation and production delivery. 

Required Qualifications:

  • Typically requires a minimum of 10 years of related experience with a Bachelor's degree; or 9 years and a Master's degree; or 7 years with a PhD; or equivalent work experience.

  • Mastery of machine learning fundamentals. 

  • Experience training an deploying ML models for computer vision in a production setting. 

  • Strong understanding of 3D vision problems/algorithms. 

  • Experience with machine learning frameworks such as PyTorch and TensorFlow. 

  • Demonstrated expertise in deploying models using TensorRT and ONNX. 

  • Proficiency in C++ and Python. 

  • Strong analytical and problem-solving skills, with the ability to translate research into practical applications. 

  • Ability to obtain a SECRET clearance 
Preferred Qualifications:

  • Experience with developing autonomous systems for defense customers. 

  • Experience with training/finetuning vision-language models, vision-language-action models, and/or world models.  

  • Contributions to open-source projects in machine learning or computer vision. 

  • Track record of publications in leading computer vision and robotics conferences and journals (e.g., CVPR, ICCV/ECCV, RAL, ICRA).

$233,760 - $350,640 a year
#LI-DS-1
#LE

Full-time regular employee offer package:
Pay within range listed + Bonus + Benefits + Equity
 
Temporary employee offer package:
Pay within range listed above + temporary benefits package (applicable after 60 days of employment)
 
Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.
 
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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know. 
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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