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Annotation Math Jobs in Seattle, WA (NOW HIRING)

Worked with methods that address accuracy and variability in human annotation data. Ability to ... Sociology, Mathematics, Physics, or similar quantitative field. Proficiency in data science ...

Annotation Math information

See Seattle, WA salary details

$25.6K

$67K

$107.5K

How much do annotation math jobs pay per year?

As of Aug 5, 2026, the average yearly pay for annotation math in Seattle, WA is $66,958.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,200.00 and $79,700.00 per year, depending on experience, location, and employer.

What is the difference between Annotation Math vs Data Annotator?

AspectAnnotation MathData Annotator
Required CredentialsBasic education, sometimes specialized training in annotation toolsHigh school diploma or equivalent, on-the-job training
Work EnvironmentData labeling teams, tech companies, remote or onsiteData labeling teams, tech companies, remote or onsite
Industry UsageAI, machine learning, data scienceAI, machine learning, data science
Common Search IntentUnderstanding roles related to data annotation and mathComparing data annotation jobs

Annotation Math and Data Annotator roles both involve data labeling within AI and machine learning industries. Annotation Math may focus more on mathematical annotations, while Data Annotator generally covers broader data labeling tasks. Both roles often share similar work environments and required skills, making them closely related in the data annotation field.

What is an annotation math job?

Annotation Math jobs involve labeling, tagging, and categorizing mathematical data, such as equations, formulas, graphs, or written math problems, to create high-quality datasets. These annotated datasets are often used to train artificial intelligence (AI) and machine learning models to recognize and process mathematical content accurately. Annotation Math professionals need a strong understanding of mathematics, attention to detail, and familiarity with annotation tools or platforms. This work is critical for improving technologies like automated math solvers, educational apps, and document digitization.

What are the key skills and qualifications needed to thrive as an annotation math specialist, and why are they important?

To thrive as an Annotation Math Specialist, you need a solid understanding of mathematics, attention to detail, and familiarity with educational or assessment standards, often supported by a relevant degree. Proficiency with annotation tools, data labeling platforms, and sometimes LaTeX or similar mathematical typesetting systems is typically required. Strong analytical thinking, communication, and the ability to work independently are essential soft skills for accuracy and consistency. These skills and qualities are crucial to ensure high-quality, precise annotations that support machine learning, educational resources, or assessment development.

What are some common challenges faced by professionals in annotation math roles, and how can they be addressed?

Professionals in Annotation Math roles often encounter challenges such as interpreting ambiguous mathematical data, maintaining consistency in labeling complex equations, and managing repetitive tasks that require high attention to detail. Addressing these challenges involves following clear annotation guidelines, collaborating with team members to resolve uncertainties, and utilizing quality assurance tools to minimize errors. Regular feedback sessions and ongoing training also help ensure accuracy and support professional growth in this specialized field.

Python Engineer (China or USA - Remote)

Braintrust

Seattle, WA • On-site

Other

Posted 7 days ago


Job description

About this role

As a Senior Python Engineer, you will work remotely on an hourly paid basis to review AI-generated Python solutions and technical explanations, as well as generate high-quality reference content that demonstrates strong software engineering practices. You will assess solutions for correctness, code quality, performance, and adherence to the prompt; identify errors in logic, architecture, or methodology; fact-check technical information; craft clear, step-by-step explanations and exemplary solutions; and rate and compare multiple AI responses based on their reasoning quality and implementation soundness. This is a fully remote, hourly paid contractor role with SME Careers, a fast-growing AI data services company and subsidiary of SuperAnnotate that provides AI training data to many of the world’s largest AI companies and foundation model labs, where your Python expertise will directly help improve the world’s premier AI models.

Your profile
  • Bachelor’s degree or higher in Computer Science, Software Engineering, Mathematics, or a closely related technical field.
  • 7+ years of professional software engineering experience, with significant hands-on work in Python on production systems.
  • Deep knowledge of Python language features, the standard library, and common ecosystems such as web frameworks, data tooling, or backend services.
  • Strong understanding of algorithms, data structures, system design, and performance optimization in Python applications.
  • Proven experience designing, reviewing, and improving high-quality Python codebases, including code review and mentoring responsibilities.
  • Minimum C1 English proficiency (written and spoken), with the ability to write clear technical explanations and follow detailed English-language guidelines.
  • Comfort working with modern tooling such as version control systems, CI/CD pipelines, testing frameworks, and containerization technologies.
  • Previous experience with AI data training, annotation, or evaluating AI-generated technical content is a strong plus.
  • Highly detail-oriented, with a structured and methodical approach to evaluating reasoning quality and identifying subtle errors in technical solutions.
  • Minimum C1 Chinese proficiency (written and spoken)
Key responsibilities
  • Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects.
  • Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.
  • Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases.