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Data Annotation Engineer Jobs in Portland, OR (NOW HIRING)

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Data Annotation Engineer information

See Portland, OR salary details

$54.6K

$156.4K

$208.9K

How much do data annotation engineer jobs pay per year?

As of May 28, 2026, the average yearly pay for data annotation engineer in Portland, OR is $156,383.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,100.00 and $207,900.00 per year, depending on experience, location, and employer.

What is a Data Annotation Engineer job?

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

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 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.
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Data Annotation Engineer jobs near you
Infographic showing various Data Annotation Engineer job openings in Portland, OR as of May 2026, with employment types broken down into 33% As Needed, and 67% Part Time. Highlights an 5% Physical, and 95% Remote job distribution, with an average salary of $156,383 per year, or $75.2 per hour.
Software Engineer, ML Infrastructure

Software Engineer, ML Infrastructure

Serve Robotics

Vancouver, WA • On-site, Remote

$155K - $190K/yr

Full-time

Posted 12 hours ago

Job description

At Serve Robotics, we're reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It's designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses.
The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries. We're looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity.
Who We Are
We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully.
As a Software Engineer on the Machine Learning (ML) Infrastructure team, you will help design, build, and maintain our petabyte-scale data and ML platform that powers data partnerships, ML research, and autonomy engineering. You will build and improve our data discovery capabilities and integrate with 3rd party annotation platforms. By collaborating with members of the autonomy and ml teams you will help us refine how we organize various data attributes and classifications. This role plays a pivotal role in helping the team leverage data from our rapidly expanding fleet of thousands of robots.
Responsibilities
  • Develop and maintain highly scalable data processing pipelines for data curation, annotation, search and ml feature extraction.
  • Build data discovery features for the platform.
  • Create and maintain search features such as natural language querying
  • Develop and maintain our orchestration and scheduling systems.
  • Maintain and evolve our data schemas such as unified data attribute system, scenario tagging and management
  • Build integrations with annotation providers to efficiently review large scale data preannotations
  • Collaborate with autonomy engineers to collect feedback, improve documentation, and run tutorials on platform features

Qualifications
  • BS or MS in computer science with focus in data engineering and/or machine learning
  • 3+ years of industry experience building, running and improving large-volume data processing, feature extraction, data annotation workflows
  • Experience building data mining and search capabilities
  • Experience with both Python and SQL is required
  • Solid understanding of data distributions and their impact on ML Models
  • Hands-on experience and good understanding of LLMs, VLMs, embeddings, vector databases
  • Experience with data annotation providers such as CVAT, LabelBox, LabelStudio, etc

What Makes You Stand Out
  • Experience with integrating cloud inference platforms for LLMs/VLMS (ChatGPT, Gemini, etc)
  • Experience working with Multi Modal data (Lidar, Camera, etc)
  • Experience with robotics systems
  • Experience optimizing large scale vector databases

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