ZipRecruiter

Log In

2

Remote Image Annotation Jobs in Washington (NOW HIRING)

aqua IT

AI/ML Engineer (Computer Vision)

Herndon, VA · On-site +1

Experience processing and augmenting image datasets at scale * 3+ years of experience with AWS ML ... Familiarity with data annotation platforms and active learning workflows for imagery * Experience ...

People also search for

All JobsRemote Image Annotation JobsRemote Image Annotation Jobs in Washington

Remote Image Annotation information

See Washington salary details

$12.2K

$67.1K

$86.2K

How much do remote image annotation jobs pay per year?

As of Jun 6, 2026, the average yearly pay for remote image annotation in Washington is $67,140.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,200.00 and $85,700.00 per year, depending on experience, location, and employer.

What are some common challenges faced by remote image annotation specialists and how can they be addressed?

Remote image annotation specialists often encounter challenges such as handling repetitive tasks, maintaining high accuracy across large volumes of images, and managing time effectively while working independently. To address these challenges, it’s important to take regular breaks to maintain focus, follow detailed annotation guidelines carefully, and communicate proactively with project managers or teammates when unclear instructions arise. Many employers provide training sessions, clear documentation, and regular feedback to help remote annotators improve their performance. Utilizing productivity tools and engaging with team forums or chat channels can also help foster collaboration and resolve questions efficiently. Over time, experience with different annotation types and toolsets can open up opportunities for advancement into quality assurance or tool administration roles.

What is a Remote Image Annotation job?

A Remote Image Annotation job involves labeling images with relevant tags, shapes, or metadata to help train machine learning models. Annotators use specialized tools to draw bounding boxes, segment objects, or add descriptive labels to improve AI accuracy. This work is typically done from home and requires attention to detail, basic computer skills, and sometimes domain-specific knowledge. It is commonly used in industries such as autonomous vehicles, healthcare, and e-commerce.

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

To thrive as a Remote Image Annotation specialist, you need keen attention to detail, strong visual analysis skills, and basic computer proficiency, often supported by a background in data entry or related fields. Familiarity with image annotation platforms and tools such as Labelbox, VGG Image Annotator, or custom client software is commonly required, though formal certifications are uncommon. Excellent time management, self-motivation, and clear communication skills help remote workers excel, especially when collaborating with distributed teams or providing task updates. These competencies ensure accurate, timely annotations that are essential for training high-quality AI and machine learning systems.

What are the most commonly searched types of Image Annotation jobs in Washington? The most popular types of Image Annotation jobs in Washington are:
What are popular job titles related to Remote Image Annotation jobs in Washington? For Remote Image Annotation jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Remote Image Annotation jobs in Washington look for? The top searched job categories for Remote Image Annotation jobs in Washington are:
What cities in Washington are hiring for Remote Image Annotation jobs? Cities in Washington with the most Remote Image Annotation job openings:
Remote Image Annotation jobs near you
Infographic showing various Remote Image Annotation job openings in Washington as of May 2026, with employment types broken down into 4% As Needed, 78% Full Time, 14% Part Time, and 4% Temporary. Highlights an 46% Physical, 1% Hybrid, and 53% Remote job distribution, with an average salary of $67,140 per year, or $32.3 per hour.

AI/ML Engineer (Computer Vision)

aqua IT

Herndon, VA • On-site, Remote

Full-time

Posted 4 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