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Remote Data Annotation Jobs in Potomac, MD (NOW HIRING)

aqua IT

AI/ML Engineer (Computer Vision)

Herndon, VA · On-site +1

... remote sensing imagery * Familiarity with electro-optical and SAR satellite imagery formats and ... Familiarity with data annotation platforms and active learning workflows for imagery * Experience ...

General Motors

Senior AI/ML Engineer

Washington, DC · On-site +1

$118K - $162K/yr

Remote/Hybrid: This role is based remotely but if you live within a 50-mile radius of Sunnyvale, CA ... data-annotation pipelines and machine-led training data solutions at foundation-model scale . We ...

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Remote Data Annotation information

What are the key skills and qualifications needed to thrive in the Remote Data Annotation position, and why are they important?

To thrive as a Remote Data Annotation specialist, strong attention to detail, accuracy, and familiarity with basic data processing concepts are essential, often requiring a high school diploma or equivalent. Experience using data labeling platforms, annotation tools (such as Labelbox or Supervisely), and sometimes familiarity with spreadsheet software may be required. Excellent time management, communication skills, and the ability to work independently are valuable soft skills in this remote role. These skills are vital to ensure that data annotations are consistent, precise, and delivered on schedule, which directly impacts the quality of AI and machine learning outcomes.

How to make $1000 a week remote?

Remote data annotation jobs typically pay per task or hour, with earnings varying based on experience, task complexity, and volume. To reach $1000 weekly, workers often need to complete a high number of tasks consistently, develop strong attention to detail, and use efficient tools or platforms that offer higher-paying projects. Building a reputation and acquiring specialized skills can also increase earning potential in this field.

Is data annotation real or fake?

Data annotation is a legitimate job involving labeling data such as images, text, or audio to train machine learning models. It requires attention to detail and familiarity with annotation tools, and it is widely used in AI development. The work is real and essential for creating accurate AI systems.

What are the typical daily tasks for someone working in Remote Data Annotation?

Daily tasks for a Remote Data Annotation role usually involve reviewing and labeling large volumes of data—such as images, audio clips, text, or video—according to specific project guidelines. You will use specialized annotation tools to identify objects, transcribe content, categorize information, or tag relevant features to support machine learning projects. Communication with project managers or quality assurance teams may be necessary for feedback and clarity on guidelines. Most roles also require regular self-checks for accuracy and the ability to meet productivity quotas or deadlines. This structure allows for a combination of focused individual work and occasional team collaboration to ensure project goals are met.

What is a Remote Data Annotation job?

A Remote Data Annotation job involves labeling, tagging, or categorizing data (such as images, text, audio, or video) to help improve machine learning models. This work is typically done from home using specialized annotation tools provided by employers. Accuracy and attention to detail are essential, as the quality of annotations directly impacts AI model performance. Many companies hire remote annotators on a freelance, part-time, or contractual basis.

Does data annotation actually pay?

Data annotation jobs, including remote roles, typically pay hourly or per task rates that can range from a few cents to several dollars per annotation, depending on the complexity and platform. Many remote data annotation positions offer consistent pay, with some requiring basic skills in data labeling tools and attention to detail. Earnings can vary based on experience, the employer, and the volume of work completed.

What is the best data annotation company to work for?

There is no definitive best data annotation company, as opportunities vary based on factors like pay, work environment, and project types. Many companies in the industry offer remote positions with flexible schedules, and job seekers should research company reviews and requirements such as attention to detail and familiarity with annotation tools. Evaluating factors like pay rates, task variety, and company reputation can help identify suitable employers for data annotation roles.
What are popular job titles related to Remote Data Annotation jobs in Potomac, MD? For Remote Data Annotation jobs in Potomac, MD, the most frequently searched job titles are:
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Remote Data Annotation jobs near you

AI/ML Engineer (Computer Vision)

aqua IT

Herndon, VA • On-site, Remote

Full-time

Posted 9 days ago

Job description

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
  • 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 infrastructure
    SageMaker Training jobs, Processing jobs, and endpoint deployment
    GPU 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 pipelines Automated benchmarking, metric computation, and result analysis
  • Experience 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