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Hourly Ai Data Annotation Jobs in Springfield, MA

micro1 AI

AI Trainer - Microbiology Expert

Hartford, CT · Remote

$70 - $90/hr

Document experimental findings and processes with a focus on clarity for AI training data ... Experience with AI, machine learning, or annotation projects related to biology or microbiology.

The Computer Merchant, Ltd

Data Engineer

Springfield, MA · On-site

$78 - $84/hr

... AI code assistants, agent‐based analytics) in a secure enterprise environment. Leadership ... While an hourly range is posted for this position, an eventual hourly rate is determined by a ...

CVS Health

AI/LLM Engineer

Hartford, CT · On-site

$101K - $203K/yr

Data Processing: Spark, Pandas * APIs & Services: FastAPI, Flask, REST/gRPC * Cloud Platforms: AWS ... hourly rate or base annual full-time salary for all positions in the job grade within which this ...

DataAnnotation

Treasury Analyst - AI Trainer

Hartford, CT · Remote

$50 - $100/hr

Projects are paid hourly starting at $50-$100+/hr, with bonus rates available on some projects ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

DataAnnotation

Financial Planner - AI Trainer

Hartford, CT · Remote

$50 - $100/hr

Projects are paid hourly starting at $50-$100+/hr, with bonus rates available on some projects ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

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Hourly Ai Data Annotation information

What is an hourly AI data annotation?

An Hourly AI Data Annotation job involves labeling, tagging, or categorizing data—such as images, text, or audio—to help train machine learning models. Annotators follow specific guidelines to ensure that the data is accurately labeled so that AI systems can learn to recognize patterns and make decisions. These jobs are typically paid by the hour and may require attention to detail, consistency, and sometimes familiarity with specialized annotation tools. This work is essential for improving the accuracy and usefulness of artificial intelligence applications.

What are the key skills and qualifications needed to thrive as an hourly AI data annotator?

To thrive as an AI Data Annotator, you need strong attention to detail, accuracy, and a basic understanding of data labeling concepts, typically supported by a high school diploma or equivalent. Familiarity with annotation tools such as Labelbox or Supervisely, and basic computer proficiency, are often required. Critical thinking, consistency, and effective communication are valuable soft skills in this role. These skills ensure high-quality, reliable data that directly improves the performance of AI and machine learning models.

What are some common challenges faced by hourly AI data annotators, and how can they be managed?

Hourly AI data annotators often encounter challenges such as repetitive tasks, maintaining high accuracy under time constraints, and adapting to evolving project guidelines. To manage these, it's important to take regular breaks to avoid fatigue, stay up to date with training materials, and communicate proactively with team leads if instructions are unclear. Many teams use collaborative tools and regular feedback sessions to support annotators and ensure consistent quality, making teamwork and attention to detail vital for success in this role.

What is the difference between Hourly Ai Data Annotation vs Data Labeler?

AspectHourly Ai Data AnnotationData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or in-office, flexible hoursRemote or in-office, flexible hours
Industry UsageAI, machine learning, tech companiesAI, machine learning, tech companies
Job FocusAnnotating data for AI training, often with specific instructionsLabeling data to help AI models learn, often similar tasks

Hourly Ai Data Annotation and Data Labeler roles are similar, focusing on preparing data for AI systems. The main difference lies in terminology; 'Hourly Ai Data Annotation' emphasizes the paid hourly aspect and the specific task of annotating data for AI training, while 'Data Labeler' is a broader term used interchangeably in the industry. Both roles require similar skills and are used in the same industry sectors.

What job categories do people searching Hourly Ai Data Annotation jobs in Springfield, MA look for?

The top searched job categories for Hourly Ai Data Annotation jobs in Springfield, MA are:

What cities near Springfield, MA are hiring for Hourly Ai Data Annotation jobs?

Cities near Springfield, MA with the most Hourly Ai Data Annotation job openings:

Hourly Ai Data Annotation jobs near you
Infographic showing various Hourly Ai Data Annotation job openings in Springfield, MA as of August 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 67% In-person, and 33% Remote job distribution.

Computer Vision & AI/ML Engineer Jobs

AIToolboard

Springfield, MA • On-site

$150 - $230/hr

Other

Posted 13 days ago

Job description

Aqua IT Springfield, US

Full-time

About the Role

Description of Services/Responsibilities:

  • Design and execute fine-tuning pipelines for Vision-Language Models (VLMs) on domain-specific imagery datasets, including data preprocessing, training orchestration, and hyperparameter optimization
  • Develop and implement evaluation frameworks for multimodal model performance, including task-specific metrics for image understanding, visual question answering, and spatial reasoning
  • Build scalable training infrastructure on AWS (SageMaker, EC2 GPU instances) for distributed fine-tuning of large multimodal models
  • Engineer data pipelines for curating, annotating, and transforming geospatial imagery datasets into model-ready formats for supervised and instruction-tuning workflows
  • Collaborate with applied scientists and solutions architects to iterate on model architectures, adapter strategies (LoRA/QLoRA), and inference optimization techniques
Basic Requirements
  • TS/SCI with CI Poly required with current NGA eligibility and SBU/SECNet/COE accounts
  • Must be willing to work in SCIF daily or as needed
  • 5+ years of professional machine learning engineering experience with a focus on deep learning
  • 1+ years of hands-on experience fine-tuning large foundation models (LLMs or VLMs)
  • Experience with parameter-efficient fine-tuning methods (LoRA, QLoRA, adapters)
  • Familiarity with supervised fine-tuning, instruction tuning, and RLHF/DPO alignment techniques
  • 4+ years of advanced Python development for ML workloads
  • Strong proficiency with PyTorch and the HuggingFace ecosystem (Transformers, PEFT, Datasets, Accelerate)
  • Experience with distributed training frameworks (DeepSpeed, FSDP, or Megatron)
  • 3+ years of experience with computer vision or multimodal models
  • Understanding of vision transformer architectures (ViT, CLIP, LLaVA-family models, or similar)
  • Experience processing and augmenting image datasets at scale
  • 3+ years of experience with AWS ML infrastructureSageMaker Training jobs, Processing jobs, and endpoint deploymentGPU instance selection, multi-node training, and cost optimization on EC2 (P4/P5/G5/G6e)S3 data management for large-scale training datasets
  • 2+ years of experience building ML evaluation pipelinesAutomated benchmarking, metric computation, and result analysisExperience with both quantitative metrics and qualitative/human evaluation approaches
  • Strong software engineering fundamentals (version control, testing, CI/CD for ML workflows)
Preferred Qualifications
  • 2+ years of experience with geospatial or remote sensing imagery
  • Familiarity with electro-optical and SAR satellite imagery formats and characteristics
  • Understanding of geospatial metadata, coordinate systems, and imagery preprocessing
  • Experience with model quantization and inference optimization (vLLM, TensorRT, ONNX)
  • Experience with MLOps and experiment tracking tools (MLflow, Weights & Biases, SageMaker Experiments)
  • Familiarity with data annotation platforms and active learning workflows for imagery
  • Experience with containerized ML workflows (Docker, ECR, ECS/EKS)
  • 2+ years of experience with Authority to Operate (ATO) processes in government environments
  • Implementation of NIST 800-53 controls and security compliance for ML systems
  • Experience deploying models in air-gapped or disconnected environments
  • Familiarity with multimodal evaluation benchmarks (MMMU, MMBench, GQA, or domain-specific equivalents)
  • Publications or demonstrated contributions in computer vision, VLMs, or multimodal AI
  • Experience with synthetic data generation for training data augmentation

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