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

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Data Annotation Research information

What are the key skills and qualifications needed to thrive as a Data Annotation Researcher, and why are they important?

To thrive as a Data Annotation Researcher, you need strong attention to detail, analytical thinking, and familiarity with data labeling concepts, often supported by a degree in computer science, linguistics, or a related field. Experience with annotation platforms, data management tools, and sometimes knowledge of programming languages like Python are typically required. Excellent communication, problem-solving abilities, and the capacity to work independently set standout contributors apart. These skills ensure high-quality, accurate data labeling, which is crucial for developing reliable AI and machine learning models.

What are some common challenges faced in Data Annotation Research roles, and how can they be addressed?

Professionals in Data Annotation Research often encounter challenges such as maintaining consistency in labeling, dealing with ambiguous data, and managing large datasets efficiently. These issues can be addressed by following detailed annotation guidelines, participating in regular calibration sessions with the team, and utilizing annotation tools that support quality control checks. Collaboration with data scientists and project managers is essential to clarify ambiguities and ensure that annotated data meets the project's requirements. Staying proactive in communication and continuous learning helps to minimize errors and improve overall data quality.

What is data annotation research?

Data annotation research involves studying and developing methods for labeling data, such as images, text, or audio, to be used in training machine learning models. Researchers in this field focus on improving annotation accuracy, efficiency, and scalability, as well as addressing challenges like bias and consistency. This work is critical because high-quality annotated data is essential for building effective AI systems. Data annotation research often includes exploring new tools, techniques, and guidelines for human annotators or automated labeling systems.

What is the difference between Data Annotation Research vs Data Labeling Specialist?

AspectData Annotation ResearchData Labeling Specialist
CredentialsTypically requires a background in data science, research methods, or related fieldsOften requires basic technical skills and experience with labeling tools
Work EnvironmentResearch labs, tech companies, or remote research teamsData centers, tech companies, or remote labeling teams
Industry UsageUsed in AI/ML research, developing annotation methodologiesUsed in preparing datasets for machine learning models
Search & Comparison IntentUnderstanding research-focused roles in data annotationLooking for practical data labeling jobs

Data Annotation Research involves exploring new annotation techniques and improving data quality for AI models, often requiring research skills. In contrast, Data Labeling Specialists focus on applying existing labeling tools to annotate datasets efficiently. Both roles are essential in AI development but differ in scope and expertise.

What are popular job titles related to Data Annotation Research jobs in Portland, OR? For Data Annotation Research jobs in Portland, OR, the most frequently searched job titles are:
What job categories do people searching Data Annotation Research jobs in Portland, OR look for? The top searched job categories for Data Annotation Research jobs in Portland, OR are:
Data Annotation Research jobs near you
Software Engineer, ML Infrastructure

Software Engineer, ML Infrastructure

Serve Robotics

Vancouver, WA • On-site, Remote

$155K - $190K/yr

Full-time

Posted 15 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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