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Data Annotation Engineer Jobs in Everett, WA (NOW HIRING)

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Machine Learning Data Engineer

Seattle, WA · Remote

$130K - $160K/yr

Qualifications: * 3+ years in a data quality, ML data engineering or applied ML role. * Experience ... Experience with dataset annotation/labeling tools and workflows (Roboflow, Labelbox, CVAT, or ...

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Data Annotation Engineer information

See Everett, WA salary details

$56.9K

$162.9K

$217.6K

How much do data annotation engineer jobs pay per year?

As of Aug 9, 2026, the average yearly pay for data annotation engineer in Everett, WA is $162,896.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,800.00 and $216,500.00 per year, depending on experience, location, and employer.

What are the main challenges faced by data annotation engineers in their daily work?

One of the main challenges Data Annotation Engineers face is ensuring consistent accuracy and quality in labeling large and often complex datasets. Attention to detail is critical, as even small errors can significantly affect machine learning model performance. Additionally, engineers must frequently adapt to evolving annotation guidelines and emerging data types, which requires ongoing learning and flexibility. Collaboration with data scientists and project managers is common to clarify requirements and resolve ambiguities, making strong communication skills essential for success.

What are the key skills and qualifications needed to thrive as a data annotation engineer?

To thrive as a Data Annotation Engineer, you need a strong background in data analysis, attention to detail, and familiarity with annotation processes, often supported by a degree in computer science or a related field. Proficiency with annotation tools like Labelbox, CVAT, or VIA, and understanding of data formats used in machine learning, is commonly required. Excellent communication, collaboration, and organizational skills help you effectively manage projects and cooperate with cross-functional teams. These abilities are crucial for delivering high-quality labeled data, which directly impacts the performance of AI and machine learning models.

What is a data annotation engineer?

A Data Annotation Engineer is responsible for labeling and annotating data—such as text, images, audio, or video—to train machine learning models. They ensure that data is accurately categorized and structured to improve model performance. This role often involves using specialized annotation tools, following detailed guidelines, and working closely with data scientists and AI teams. Data Annotation Engineers play a crucial role in the development of AI applications by providing high-quality labeled datasets for supervised learning.

What job categories do people searching Data Annotation Engineer jobs in Everett, WA look for? The top searched job categories for Data Annotation Engineer jobs in Everett, WA are:
What cities near Everett, WA are hiring for Data Annotation Engineer jobs? Cities near Everett, WA with the most Data Annotation Engineer job openings:
Data Annotation Engineer jobs near you
Infographic showing various Data Annotation Engineer job openings in Everett, WA as of August 2026, with employment types broken down into 68% Full Time, 9% Part Time, and 23% Contract. Highlights an 72% In-person, and 28% Remote job distribution, with an average salary of $162,896 per year, or $78.3 per hour.

Machine Learning Engineer - Computer Vision & Data Systems

Socket.dev

Seattle, WA • On-site

$140 - $200/hr

Other

Posted 4 days ago

Job description

At Apple, we are dedicated to creating technologies that enrich people's lives. Our teams develop products and experiences that empower millions of users globally, by combining world-class engineering with a deep commitment to innovation, quality, and privacy. We are seeking a Machine Learning Engineer with strong expertise in computer vision and large-scale data processing. In this role, you will contribute to the development of next-generation real-time sensing and data intelligence systems by designing algorithms, building scalable data pipelines, and collaborating with multi-functional teams to deliver high-impact, production-quality solutions.

DESCRIPTION
  • Design, build, and maintain large-scale data processing workflows, ensuring efficiency, scalability, and reliability across diverse data sources and modalities.
  • Develop and optimize computer vision models that power core product experiences, including areas such as image understanding, multi-view geometry, 3D reconstruction, and visual recognition.
  • Partner closely with engineering, research, and data teams to translate product requirements into technical solutions. This includes prototyping models, running large-scale experiments, improving data quality, and ensuring seamless integration of algorithms into production systems.
  • Explore emerging areas such as LLM-based agents, retrieval-augmented systems, and tool-oriented reasoning to improve internal workflows or data operations.
MINIMUM QUALIFICATIONS
  • Strong foundation in computer vision, including experience with deep learning–based vision models and at least one area such as detection, segmentation, 3D vision, geometric methods, tracking, or self-supervised learning.
  • Hands-on experience developing machine learning models using frameworks such as PyTorch or TensorFlow.
  • Experience building or optimizing large-scale data pipelines (e.g., distributed ETL, dataset generation, annotation workflows, data validation, or high-throughput processing).
  • Proficiency in Python or C++ for algorithm development and data processing.
  • Experience working with distributed computing frameworks (e.g., Spark, Ray, or equivalent).
PREFERRED QUALIFICATIONS
  • PhD in a relevant field with research directly related to computer vision, large-scale data systems, or multimodal learning.
  • Experience designing or evaluating agentic systems, including LLM-powered tools, RAG pipelines, or automated data reasoning workflows.
  • Familiarity with prompt engineering, tool-use patterns, and LLM model behavior.
  • Experience deploying ML models at scale, including monitoring, evaluation, and continuous improvement.
  • Knowledge of data quality assessment, dataset curation methodologies, and evaluation frameworks.
  • Experience with GPU-based optimization, large-batch training, or distributed training.
  • Strong multi-functional collaboration skills and the ability to lead technical initiatives.
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