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Synthetic Data Generation Jobs in Seattle, WA (NOW HIRING)

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Synthetic Data Generation information

See Seattle, WA salary details

$35.3K

$106.1K

$192.3K

How much do synthetic data generation jobs pay per year?

As of Sep 5, 2026, the average yearly pay for synthetic data generation in Seattle, WA is $106,061.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,000.00 and $164,400.00 per year, depending on experience, location, and employer.

What is synthetic data generation?

Synthetic data generation is the process of creating artificial datasets that mimic real-world data. This technique is used to supplement or replace actual data for purposes such as machine learning, software testing, and research, especially when real data is scarce, sensitive, or costly to obtain. Synthetic data can help improve model accuracy, protect privacy, and enable innovation by providing diverse and unbiased datasets. It is commonly used in fields like healthcare, finance, and autonomous vehicles.

What are the key skills and qualifications needed to thrive in synthetic data generation?

To excel in a Synthetic Data Generation role, you need a solid background in computer science, statistics, and data science, often supported by a relevant degree and experience in machine learning. Familiarity with tools such as Python, TensorFlow, PyTorch, and synthetic data generation platforms, as well as knowledge of privacy-preserving techniques, is typically required. Strong problem-solving abilities, creativity, and effective communication set top performers apart in this field. These skills and qualities are crucial for creating high-quality, realistic synthetic datasets that support robust AI model development while safeguarding sensitive information.

What are the main challenges faced by professionals working in synthetic data generation, and how can they be addressed?

Professionals in synthetic data generation often encounter challenges such as ensuring the generated data accurately represents real-world scenarios while maintaining privacy and data security. Balancing realism with anonymization is crucial, especially when synthetic data is used for AI model training or testing. Collaboration with data scientists, domain experts, and privacy officers is common to validate data utility and compliance with regulations. Staying current with advances in generative models and data validation techniques also helps address these challenges and contributes to career growth in this rapidly evolving field.

What is the difference between Synthetic Data Generation vs Data Analyst?

AspectSynthetic Data GenerationData Analyst
Required CredentialsKnowledge of data science, programming, and data privacyDegree in statistics, data science, or related field
Work EnvironmentData science teams, research labs, tech companiesBusiness environments, analytics teams, consulting firms
Industry UsageAI development, machine learning, data privacyBusiness insights, reporting, decision-making
Search & Comparison IntentUnderstanding data generation techniques, privacy solutionsAnalyzing data, generating reports, insights

While Synthetic Data Generation focuses on creating artificial data for privacy and model training, Data Analysts interpret existing data to provide business insights. Both roles require data-related skills but serve different purposes within the data ecosystem.

What are popular job titles related to Synthetic Data Generation jobs in Seattle, WA?

For Synthetic Data Generation jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Synthetic Data Generation jobs in Seattle, WA look for?

The top searched job categories for Synthetic Data Generation jobs in Seattle, WA are:

What cities near Seattle, WA are hiring for Synthetic Data Generation jobs?

Cities near Seattle, WA with the most Synthetic Data Generation job openings:

Synthetic Data Generation jobs near you
Infographic showing various Synthetic Data Generation job openings in Seattle, WA as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $106,061 per year, or $51 per hour.

AI Researcher - Large Language Models (LLMs)

ResourceWell

Redmond, WA • On-site

Full-time

Medical, Retirement

Re-posted 4 days ago

Job description

AI Researcher - Large Language Models (LLMs) Join our client's Research and Development (AI/ML Team) in Redmond, where innovation meets impact. Their team is at the forefront of AI research, specialising in cutting-edge advancements in Large Language Models (LLMs) and generative AI. They offer a dynamic, collaborative environment where visionary researchers drive transformative projects with real-world impact.

What You'll Do As an AI Researcher, you will: Conduct pioneering research on LLMs, focusing on architecture, training, optimization, and fine-tuning methodologies. Design and implement advanced data preparation workflows, including data cleaning, augmentation, and synthetic data generation. Develop scalable training pipelines for LLMs using distributed computing and state-of-the-art optimization algorithms.

Explore multi-modal AI systems, integrating LLMs with other data types such as vision and audio. Publish high-impact research in top-tier journals and conferences like IEEE, enhancing the field of AI. Mentor junior researchers, sharing best practices to inspire impactful AI innovations.

Stay at the cutting edge of LLM advancements, fostering continuous learning and innovation within the team. Requirements Required Qualifications: Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field, with research emphasis on NLP, LLMs, or generative AI

Demonstrated expertise in training and fine-tuning large-scale language models (e.g., GPT, BERT, T5). Strong publication record in IEEE or equivalent journals/conferences related to LLM training, data preparation, or synthetic data generation. Proficiency in deep learning frameworks such as TensorFlow, PyTorch, or JAX

Hands-on experience in large-scale data processing and distributed training techniques. Preferred Qualifications: Experience with RAG and multi-modal AI systems. Expertise in domain-specific LLM fine-tuning and data augmentation.

Familiarity with synthetic data generation tools and platforms like Apache Spark or Dask. Proven leadership and mentoring abilities in a research settings. Benefits What Our Client Offers Competitive Salary: Reflective of your expertise and experience.

Cutting-Edge Infrastructure: Access to state-of-the-art computational resources for large-scale AI model training. Professional Growth: Opportunities to publish in prestigious venues and collaborate with leading AI experts. Real-World Impact: Be a part of transformative AI projects shaping industries.

Comprehensive Benefits: Robust health coverage, retirement plans, and more. Why Join Our Client. By joining, you will: Lead groundbreaking research in the field of LLMs and generative AI.

Collaborate with a team of industry-leading AI researchers. Drive innovation in a supportive and dynamic environment. Contribute to projects with significant societal and industrial implications.

Next Steps Ready to revolutionize the future of AI. Apply today. Applications will be reviewed on a rolling basis.

Location This position is based in Redmond. Equal Opportunity We value diversity and are committed to creating an inclusive environment for all employees.

Apply