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Assistant Ai Trainer Jobs (NOW HIRING)

DataAnnotation

Finance Director - AI Trainer

Concord, CA · Remote

$50 - $100/hr

We're currently expanding into an exciting new area - teaching AI Assistant models to be a more ... To succeed in this position, you should have expert-level financial reasoning and formal training ...

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Assistant Ai Trainer information

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$70K

How much do assistant ai trainer jobs pay per year?

As of Sep 5, 2026, the average yearly pay for assistant ai trainer in the United States is $42,039.00, according to ZipRecruiter salary data. Most workers in this role earn between $24,000.00 and $50,500.00 per year, depending on experience, location, and employer.

What does an assistant AI trainer do?

An Assistant AI Trainer helps improve artificial intelligence systems by providing feedback, curating datasets, and evaluating model outputs. They often review and label data, test AI responses, and suggest improvements to ensure the AI behaves in a useful and ethical manner. This role is important for refining how AI models understand and interact with human inputs across various applications.

What are the key skills and qualifications needed to thrive as an assistant AI trainer, and why are they important?

To thrive as an Assistant AI Trainer, you need a solid understanding of data annotation, basic machine learning concepts, and strong attention to detail, often supported by a relevant degree or coursework. Familiarity with annotation tools, data management systems, and sometimes basic programming or scripting skills is typically required. Excellent communication, critical thinking, and the ability to follow detailed guidelines set top performers apart. These skills ensure the production of high-quality datasets and accurate model training, which are vital for developing effective AI systems.

What are the main challenges an assistant AI trainer faces when annotating and reviewing training data?

Assistant AI Trainers often encounter challenges such as ensuring consistency and accuracy in data annotation, especially when guidelines evolve or tasks are subjective. Balancing speed with quality can be difficult, as there may be tight deadlines for large datasets. Additionally, collaborating with data scientists and senior trainers to clarify ambiguous cases or edge scenarios is common, requiring strong communication skills. Staying updated with the latest tool updates and annotation standards is also essential for success in this role.

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

AspectAssistant Ai TrainerData Annotator
Required CredentialsBasic technical skills, training in AI conceptsAttention to detail, basic computer skills
Work EnvironmentCollaborative, office or remoteRemote or on-site, often repetitive tasks
Industry UsageAI development, machine learning projectsData labeling, dataset preparation
Common Search IntentUnderstanding AI training rolesData labeling jobs

Assistant Ai Trainers focus on training AI models by providing feedback and refining algorithms, requiring some technical knowledge. Data Annotators primarily label and categorize data, emphasizing accuracy and attention to detail. While both roles support AI development, Assistant Ai Trainers have a broader scope involving model improvement, whereas Data Annotators concentrate on data preparation.

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Assistant Ai Trainer jobs near you
Infographic showing various Assistant Ai Trainer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, 1% Temporary, and 2% Contract. Highlights an 98% Physical, 1% Hybrid, and 1% Remote job distribution, with an average salary of $42,039 per year, or $20.2 per hour.

Engineering Assistant- AI Based Solutions

FEV North America Inc

Auburn Hills, MI • On-site

Full-time

Re-posted 21 days ago

Job description

Description

  • Develop and deploy AI-based solutions to automate engineering and business workflows, improving efficiency, decision-making, and productivity
  • Identify opportunities to replace manual or rule-based processes with intelligent AI-driven workflows and agents
  • Design, develop, and integrate machine learning (ML), reinforcement learning (RL), and generative AI models for engineering applications
  • Replace conventional rule-based control algorithms and physics-based functions with data-driven ML/RL models where appropriate
  • Develop data pipelines for collection, cleaning, feature engineering, training, validation, and deployment of AI models
  • Train, optimize, and validate ML/RL models using simulation, test, and field data
  • Collaborate with controls, software, systems, and domain experts to integrate AI models into production systems
  • Support development of digital twins, predictive analytics, anomaly detection, optimization, and intelligent decision-making systems
  • Monitor model performance, perform retraining activities, and ensure robustness and scalability of deployed solutions
  • Prepare technical reports, documentation, presentations, and demonstrations for internal and customer stakeholders
  • Stay current with emerging AI technologies, frameworks, and best practices and evaluate their applicability to engineering challenges

Requirements

  • Working towards Bachelor's or master's degree in computer science, Electrical Engineering, Mechanical Engineering, Robotics, Data Science, Artificial Intelligence, or a related field
  • Understanding of Machine Learning, Deep Learning, Reinforcement Learning, and Generative AI concepts
  • Proficiency in Python and common AI/ML frameworks such as TensorFlow, PyTorch, and RL libraries
  • Experience with software development tools, version control systems, and CI/CD processes
  • Experience with data processing, feature extraction, model training, validation, and deployment workflows

Preferred Qualifications:

  • Knowledge of optimization techniques, control systems, and system modeling concepts
  • Familiarity with cloud-based AI platforms and MLOps practices is preferred
  • Strong analytical and problem-solving skills with the ability to work on complex engineering challenges
  • Professional communication skills (oral and written) and ability to present technical concepts to diverse audiences

 Equal opportunity employer as to all protected groups, including protected veterans and individuals with disabilities 

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