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Junior Training Ai Models Jobs (NOW HIRING)

Kaspar Companies

AI Specialist

Shiner, TX · On-site

This role is responsible for training AI models, implementing AI solutions, and enhancing manufacturing processes through innovative technologies. Responsibilities : • Train and refine AI models ...

NVIDIA

Solutions Architect, AI Models

Santa Clara, CA · On-site

$74 - $97.50/hr

... training, post-training, reinforcement learning (RL), evaluation, and model optimization. • ... AI solutions. • As we work with customers across multiple industries, we help improve NVIDIA ...

Photon

GenAI Solution Architect - VA

Norfolk, VA · On-site

$61 - $80.25/hr

R Knowledge of state-of-the-art generative AI models such as GPT-3, DALL-E, and CLIP. Experience with Cloud infrastructure and Platforms - Azure /GCP/AWS Experience with training and evaluating large ...

Photon

GenAI Solution Architect - VA

Norfolk, VA

$61 - $80.25/hr

R Knowledge of state-of-the-art generative AI models such as GPT-3, DALL-E, and CLIP. Experience with Cloud infrastructure and Platforms - Azure /GCP/AWS Experience with training and evaluating large ...

Nvidia

Solutions Architect, AI Models

Santa Clara, CA

$74 - $97.50/hr

Tackle sophisticated AI challenges by applying skills across the AI model lifecycle-from data processing and orchestration to training, post-training, reinforcement learning (RL), evaluation, and ...

Momento USA LLC

Apply labels to collected data for the purpose of training AI models for in field scam/deepfake detections * Perform data cleaning, validation, and transformation with analytical tools and ...

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Junior Training Ai Models information

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$33.5K

$71.8K

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How much do junior training ai models jobs pay per year?

As of Aug 9, 2026, the average yearly pay for junior training ai models in the United States is $71,799.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,500.00 and $80,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced by junior training AI models professionals and how can they be addressed?

Junior Training AI Models often face challenges such as ensuring high-quality data labeling, understanding complex annotation guidelines, and maintaining consistency across large datasets. To address these, it’s important to ask clarifying questions, actively participate in team discussions, and utilize feedback from senior annotators or quality assurance leads. Regular communication with team members and ongoing learning about AI model requirements can significantly improve accuracy and confidence in the role.

What is the difference between Junior Training Ai Models vs Data Annotators?

AspectJunior Training Ai ModelsData Annotators
Required CredentialsBasic understanding of AI/ML concepts, sometimes a degree in computer science or related fieldTypically high school diploma or equivalent; training provided on annotation tools
Work EnvironmentCollaborative teams, often in tech companies or AI startupsData labeling centers, remote or onsite
Industry UsageAI development, machine learning projects, data preparationData labeling, data quality assurance
Common Search/ComparisonYesYes

Junior Training Ai Models involve developing and fine-tuning AI systems, requiring some technical knowledge. Data Annotators focus on labeling data to train AI models, often with minimal technical background. Both roles are essential in AI projects but differ in responsibilities and skill requirements.

What does a junior training AI models professional do?

A Junior Training AI Models professional assists in preparing, labeling, and organizing datasets used to train artificial intelligence algorithms. They may help ensure data quality, follow annotation guidelines, and support more senior data scientists or machine learning engineers in developing and refining AI models. This entry-level role is crucial for ensuring that AI systems learn from accurate and relevant information. Juniors might also help with testing models, reporting issues, and suggesting improvements to training processes.

What are the key skills and qualifications needed to thrive as a junior training AI models professional?

To thrive as a Junior Training AI Models Specialist, you typically need foundational knowledge in computer science, data analysis, and machine learning concepts, often supported by a relevant degree or coursework. Familiarity with programming languages like Python, machine learning frameworks (such as TensorFlow or PyTorch), and data annotation tools is commonly required. Attention to detail, problem-solving skills, and effective communication are standout soft skills for this role. These competencies are crucial for accurately training models, collaborating with technical teams, and ensuring high-quality AI outputs.
What cities are hiring for Junior Training Ai Models jobs? Cities with the most Junior Training Ai Models job openings:
What are the most commonly searched types of Training Ai Models jobs? The most popular types of Training Ai Models jobs are:
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Junior Training Ai Models jobs near you

Drone Pilot for AI Training and Data Collection

TSMG

San Francisco, CA

Full-time

Re-posted 5 days ago

Job description

We are seeking an experienced and highly skilled Drone Pilot to assist in the development and training of AI models, specifically in the areas of computer vision, sensor fusion, and autonomous navigation. The ideal candidate will be responsible for collecting high-quality data in real-world environments, contributing to the optimization of AI algorithms that power autonomous systems.

As a Drone Pilot for AI & Autonomous Systems Training, you will operate advanced drones to capture data essential for training and validating AI models that power autonomous navigation, object detection, and decision-making systems.
Key Responsibilities:
  • Pilot drones equipped with high-resolution cameras, LiDAR, thermal imaging, and other sensors to collect diverse datasets used for training AI algorithms, particularly for autonomous navigation, computer vision, and sensor fusion.
  • Capture data in various real-world conditions (e.g., urban, rural, industrial, challenging weather conditions) to expose AI systems to a wide range of environments and scenarios.
  • Execute complex drone missions with precise data collection objectives, such as aerial mapping, 3D reconstruction, obstacle detection, and object tracking.
  • Collaborate closely with AI engineers, machine learning specialists, and autonomous systems teams to ensure data collection aligns with the specific requirements of AI model training.
  • Perform post-flight data quality checks and initial preprocessing to ensure the datasets are ready for use in training AI models.
  • Operate drones in both manual and autonomous modes, supporting AI-driven flight operations where drones rely on onboard algorithms for navigation and decision-making.
  • Adhere to Federal Aviation Administration (FAA) and local aviation regulations governing drone operations, ensuring the safe and compliant conduct of all drone missions.
  • Oversee the maintenance, calibration, and troubleshooting of drones and onboard sensors to ensure the highest standards of performance and reliability.
Qualifications:
  • Commercial drone pilot certification (FAA Part 107 or equivalent), with additional certifications in safety or advanced drone technologies considered a plus.
  • Proven track record as a drone pilot, with significant experience in collecting data for industrial, research, or AI-focused applications.
  • Expertise in flying drones equipped with advanced sensors such as LiDAR, thermal cameras, RGB cameras, and multispectral sensors.
  • Familiarity with the nuances of autonomous flight, sensor integration, and machine learning workflows, especially those that involve real-time data processing.
  • Strong understanding of how drone-collected data is used for AI training, including its role in training AI for perception, navigation, and decision-making.
  • Experience in using software for flight planning, such as Pix4D, DroneDeploy, or similar platforms, and geospatial data analysis tools.
  • Familiarity with machine learning concepts, especially those related to computer vision (e.g., image segmentation, object detection, and tracking) and autonomous navigation systems.
  • Basic understanding of geospatial data processing, photogrammetry, and 3D reconstruction techniques for AI applications.
  • Strong attention to detail with a commitment to ensuring the highest quality of data collection and analysis.
  • Excellent communication and collaboration skills, with the ability to work in multidisciplinary teams.
We would be happy to get to know you and your skills better and see how we can support each other's growth.

Please apply and let's meet!
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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