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Freelance Machine Learning Data Annotation Jobs in Seattle, WA

Senior Machine Learning Scientist

Seattle, WA · Remote

$104K - $142K/yr

Our Machine Learning and Data Science team is growing. We are looking for a Senior Machine Learning Scientist to help tackle some of the most complex customer experience problems in the travel domain.

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

Collaborate with data scientists and flight software engineers to integrate AI capabilities into ... Proven experience deploying machine learning models into production. * Strong software engineering ...

New

Machine Learning Engineer

Seattle, WA · On-site

$165K - $209K/yr

... engineering, machine learning engineering, or related roles. * Data Pipelineexperience ... annotation or operations teams. Why Cisco? At Cisco, we're revolutionizing how data and ...

New

We're looking for an exceptional Machine Learning Engineer to help shape the future of our core ... Data Analysis and Insight Generation : Analyze experimental data to extract actionable insights.

Machine Learning Manager In order to execute our vision, we're constantly growing our machine ... Participate in the full development cycle: data collection, labeling, model development ...

Machine Learning Manager

Seattle, WA · On-site

$180K - $250K/yr

Machine Learning Manager In order to execute our vision, we're constantly growing our machine ... Participate in the full development cycle: data collection, labeling, model development ...

We're looking for an exceptional Machine Learning Engineer to help shape the future of our core ... Data Analysis and Insight Generation : Analyze experimental data to extract actionable insights.

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

Machine Learning Role In order to execute our vision, we need to grow our team of best-in-class ... Maintain awareness of industry best practices for data maintenance handling as it relates to your ...

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

Machine Learning Role In order to execute our vision, we need to grow our team of best-in-class ... Maintain awareness of industry best practices for data maintenance handling as it relates to your ...

Study and transform data science prototypes * Design machine learning systems * Research and ... implement appropriate ML algorithms and tools * Develop machine learning applications according to ...

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Freelance Machine Learning Data Annotation information

See Seattle, WA salary details

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$39

How much do freelance machine learning data annotation jobs pay per hour?

As of Jul 7, 2026, the average hourly pay for freelance machine learning data annotation in Seattle, WA is $24.89, according to ZipRecruiter salary data. Most workers in this role earn between $19.71 and $28.46 per hour, depending on experience, location, and employer.

What is the difference between Freelance Machine Learning Data Annotation vs Data Labeler?

AspectFreelance Machine Learning Data AnnotationData Labeler
CredentialsBasic understanding of annotation tools, sometimes with specialized domain knowledgeTypically no formal credentials required
Work EnvironmentRemote, flexible, project-basedOften remote or in-house, depending on employer
Industry UsageUsed in AI/ML development for training datasetsUsed in data preparation for various industries, including AI
Search/Comparison IntentFocuses on freelance opportunities, project scope, and toolsMore general, often employed by companies for data labeling tasks

Freelance Machine Learning Data Annotation involves independently completing annotation tasks for AI models, often with specialized tools and domain knowledge. Data Labelers typically perform similar tasks but may work as employees or contractors within a company. The main difference lies in the freelance nature and project-based work of data annotation roles.

What are the key skills and qualifications needed to thrive as a Freelance Machine Learning Data Annotation specialist, and why are they important?

To thrive as a Freelance Machine Learning Data Annotation specialist, you need attention to detail, basic knowledge of data labeling concepts, and familiarity with machine learning data types. Experience with annotation tools (such as Labelbox, RectLabel, or CVAT) and understanding of data privacy protocols are commonly required. Strong communication, time management, and the ability to follow complex guidelines are essential soft skills for delivering accurate results. These skills ensure high-quality, consistent data annotation, which is critical for effective machine learning model training and performance.

What is freelance machine learning data annotation?

Freelance machine learning data annotation involves labeling or tagging data—such as images, text, audio, or video—to help train machine learning models. As a freelancer, you work independently or through platforms, completing specific annotation tasks assigned by companies or researchers. This work is essential because high-quality labeled data is required for AI systems to learn and make accurate predictions. Annotators may categorize images, transcribe speech, or highlight relevant information in documents. The flexibility of freelancing allows you to choose projects and work remotely.

What are some common challenges faced by freelance machine learning data annotators, and how can they be managed?

Freelance machine learning data annotators often encounter challenges such as maintaining data accuracy, handling repetitive tasks, and understanding complex annotation guidelines. Staying organized and regularly reviewing project instructions can help ensure consistency and quality in annotations. Additionally, communicating proactively with project managers and utilizing annotation tools efficiently can help manage workload and clarify uncertainties. Building expertise in different data types (text, image, audio) also allows annotators to diversify their projects and reduce monotony.
What are the most commonly searched types of Machine Learning Data Annotation jobs in Seattle, WA? The most popular types of Machine Learning Data Annotation jobs in Seattle, WA are:
What are popular job titles related to Freelance Machine Learning Data Annotation jobs in Seattle, WA? For Freelance Machine Learning Data Annotation jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Freelance Machine Learning Data Annotation jobs in Seattle, WA look for? The top searched job categories for Freelance Machine Learning Data Annotation jobs in Seattle, WA are:
Senior Machine Learning Scientist

Senior Machine Learning Scientist

Expedia

Seattle, WA • Remote

$104K - $142K/yr

Full-time

Medical, Dental, Vision, PTO

Re-posted 21 days ago


Expedia Group rating

7.9

Company rating: 7.9 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

5th of 11 rated travel agencies


Job description

At Expedia Group, we help travelers explore the world, one journey at a time. As a global travel company powered by passionate people, trusted partnerships, and leading technology, we connect travelers, partners, and advertisers through our consumer brands, B2B network, and travel advertising business.


Here, you'll do meaningful work that helps millions of people discover, book, and experience travel with more ease, confidence, and joy. Our five Behaviors-Traveler First, Think Big, Operate with Excellence, Ownership Mindset, and Succeed Together-help foster a supportive environment where people can grow their careers and have the flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere.

Senior Machine Learning Scientist

The Senior Machine Learning Scientist is responsible for building and evaluating GenAI- and LLM-powered solutions and AI agents that improve post-booking customer experience, including recommendations, customer service, and trip management. Owns end-to-end ML and GenAI projects-from problem framing and data preparation through model/agent design, orchestration, deployment, and continuous evaluation. Applies deep expertise in applied ML, Generative AI, and rigorous experimentation to design robust evaluation frameworks (A/B tests, offline metrics, qualitative assessments) that ensure agents are safe, effective, and aligned with business goals. Partners closely with product, engineering, and operations while mentoring junior scientists and helping define best practices for AI agent development and evaluation.

Are you passionate about using machine learning to improve customer experience at scale? Would you like to work in the fast-paced, competitive, customer-focused, and data-rich world of online travel?

Our Machine Learning and Data Science team is growing. We are looking for a Senior Machine Learning Scientist to help tackle some of the most complex customer experience problems in the travel domain. You will develop state-of-the-art machine learning and AI solutions to power and enhance the customer experience across highly complex postbooking recommendations, customer service, and trip management use cases.

You will tackle substantial technical challenges, from inference problems on long-tail traveler data to multi-objective optimization in a highly dynamic, operationally complex customer service environment. Your passion for the craft of machine learning, causal inference, and Generative AI will unlock tangible growth for our business by exploiting rich datasets and building effective solutions for travelers and our partners.

This is your opportunity to build core algorithms that help Expedia Group's Post Booking organization bring context and intelligence to every step of the traveler journey and redefine what service excellence in travel can be. We are looking for a hands-on senior scientist who can independently drive impactful projects, mentor others, and collaborate closely with partners to make travel more seamless for millions of customers and partners worldwide.

In this role, you will:

Design & Implement ML Solutions

  • Own the end-to-end ML lifecycle for medium-to-large projects: from problem framing and ideation through research, prototyping, deployment, and post-launch monitoring.

  • Design robust, scalable ML systems (batch and/or streaming) in partnership with engineering, including data pipelines, feature computation, and model serving.

  • Translate ambiguous business problems into well-defined ML problems with clear success metrics and validation strategies.

Applied Machine Learning & Data Science

  • Develop, evaluate, and iterate on supervised, unsupervised, and deep learning models for prediction, recommendation, and optimization.

  • Apply causal inference and experimental design (A/B testing) to accurately measure impact and guide decision-making.

  • Read and apply relevant academic and industry research to improve model architectures, training strategies, and evaluation methods.

  • Contribute to defining best practices for experimentation and modeling within the team; help raise the technical bar for ML development.

Generative AI & Advanced Techniques

  • Build and iterate on models and applications leveraging GenAI / LLM technologies (e.g., OpenAI, Hugging Face, Anthropic, Gemini) for customer support, content generation, and workflow automation.

  • Use prompting, retrieval-augmented generation, and tool/function-calling patterns to integrate LLMs into production systems.

  • Explore and prototype advanced ML techniques (e.g., reinforcement learning, sequence modeling, transformers) where they can provide clear business value.

Statistics, Experimentation & Model Design

  • Design end-to-end modeling approaches, including data selection, feature engineering, algorithm choice, training procedures, and evaluation.

  • Apply statistical rigor in analyzing experiments and observational data; quantify uncertainty, trade-offs, and model risk.

  • Define and monitor offline and online metrics that faithfully reflect business goals (e.g., customer satisfaction, cost-to-serve, operational efficiency).

Collaboration, Communication & Visualization

  • Partner closely with product managers, engineers, analysts, and operations to understand requirements, define roadmaps, and align on priorities.

  • Communicate complex technical concepts in a clear, concise way to technical and non-technical stakeholders.

  • Build intuitive dashboards and visualizations to explain model behavior, experiment results, and business impact.

Stakeholder & Project Management

  • Lead cross-functional projects involving multiple partners (e.g., product, engineering, operations), driving them from conception to measurable impact.

  • Manage project scope, timelines, and communication, proactively surfacing risks and trade-offs.

  • Mentor junior scientists and engineers on modeling approaches, experimentation, and analytical problem solving.

Experience & Qualifications:

Experience & Education

  • PhD in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics, Economics, Operations Research) and ~3+ years of industry experience;
    or Master's degree in a quantitative field with ~5+ years of relevant industry experience.

  • Proven track record of building and deploying ML models that meaningfully impact business metrics in a production environment.

Functional & Technical Skills

Applied ML & Statistics

  • Strong knowledge of machine learning theory and practice (e.g., supervised learning, representation learning, ranking/recommendation, deep learning).

  • Solid grounding in statistics, experimental design (A/B testing), and basic causal inference; comfortable designing and analyzing online experiments.

  • Able to design end-to-end ML solutions: frame the problem, choose data sources, select algorithms, define evaluation strategies, and iterate based on results.

Engineering & Tooling

  • Strong programming skills in Python and its data/ML ecosystem (e.g., pandas, scikit-learn, PyTorch/TensorFlow, PySpark), plus proficiency in SQL.

  • Experience working with cloud-based data/compute platforms and modern data/ML tooling (e.g., Spark, Airflow, feature stores, model serving frameworks).

  • Follow software engineering best practices (version control, code reviews, testing, documentation) and contribute to shared libraries and tooling.

Generative AI & Advanced Methods

  • Hands-on experience using GenAI / LLM APIs (e.g., OpenAI, Hugging Face, Anthropic, Gemini) in prototypes or production is highly desired.

  • Familiarity with concepts like prompt engineering, retrieval-augmented generation, function/tool calling, and evaluation of LLM-based systems.

  • Experience with reinforcement learning, bandits, or other advanced ML techniques is a plus.

Problem Solving & Communication

  • First-principles problem solver: able to decompose ambiguous problems, identify key assumptions, and design pragmatic, iterative solutions.

  • Excellent written and verbal communication skills; able to tell a compelling story with data and models and influence decisions.

  • Collaborative and customer-obsessed, with the ability to balance scientific rigor and engineering pragmatism in a product environment.

Highly Desired Experience

  • Domain experience in customer service, recommendations, personalization, or e-commerce applications.

  • Experience building ML systems for operational decision-making (e.g., contact routing, triage, capacity/effort prediction, workflow optimization).

  • Experience mentoring other scientists or engineers and contributing to technical culture (e.g., brown bags, tech talks, documentation, best practices).

If you're excited about building impactful ML and AI solutions that improve how millions of travelers are served every day, we'd love to hear from you.

The total cash range for this position in Seattle is $173,000.00 to $242,500.00. Employees in this role have the potential to increase their pay up to $277,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.The total cash range for this position in San Jose is $187,000.00 to $261,500.00. Employees in this role have the potential to increase their pay up to $299,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.

Starting pay for this role will vary based on multiple factors, including location, available budget, and an individual's knowledge, skills, and experience. Pay ranges may be modified in the future.

Benefits and perks

Expedia Group offers benefits and perks designed to support employees and their families, including medical, dental, and vision coverage, paid time off, an Employee Assistance Program, wellness and travel reimbursement, travel discounts, and International Airlines Travel Agent Network (IATAN) membership. Learn more about life at Expedia Group at https://careers.expediggroup.com/life.


Accommodation requests

Expedia Group is committed to providing an inclusive and accessible recruiting experience. If you need an accommodation or adjustment due to a disability during the application or recruiting process, please submit a request at https://expedia.service-now.com/askeg?id=job_accommodation.


About Expedia Group

Expedia Group includes three flagship consumer brands - Expedia, Hotels.com, and Vrbo - along with a leading B2B travel business and travel advertising offerings. Across our brands and business, we help travelers explore the world with confidence and ease.


Important notice

Employment opportunities and job offers at Expedia Group will always come from Expedia Group's Talent Acquisition and hiring teams. Never share sensitive personal information unless you are confident of the recipient. Expedia Group does not extend job offers via email or messaging tools to individuals with whom we have not made prior contact. Our email domain is @expediagroup.com. The official place to find and apply for roles is https://careers.expediagroup.com/jobs/.


Equal Opportunity

Expedia is committed to creating an inclusive work environment with a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or any other characteristic protected by law. This employer participates in E-Verify. The employer will provide the Social Security Administration (SSA) and, if necessary, the Department of Homeland Security (DHS) with information from each new employee's I-9 to confirm work authorization.

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