ZipRecruiter

Log In

1

Synthetic Data Generation Jobs (NOW HIRING)

NT Concepts

Data Scientist

Herndon, VA · On-site +1

Working within a cross-functional team and reporting to a technical lead, you will operate across the machine learning development lifecycle, from data curation and synthetic data generation to model ...

Grid Dynamics Holdings

ML Applied Scientist

Synthetic Data Generation: Develop and maintain synthetic data generation pipelines to augment evaluation coverage, stress-test safety boundaries, and support evaluation in low-resource languages.

Nvidia

OR · On-site

Research experience in at least one of: generative modeling, synthetic data generation, LLM post-training (SFT/RLHF/DPO/RL), reward modeling, multi-agent or interactive simulation, behavioral or ...

NT Concepts

Data Scientist

Herndon, VA · On-site

Working within a cross-functional team and reporting to a technical lead, you will operate across the machine learning development lifecycle, from data curation and synthetic data generation to model ...

next page

Showing results 1-20

People also search for

All JobsSynthetic Data Generation Jobs

Synthetic Data Generation information

See salary details

$31K

$93.2K

$169K

How much do synthetic data generation jobs pay per year?

As of Jun 7, 2026, the average yearly pay for synthetic data generation in the United States is $93,198.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,500.00 and $144,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in a Synthetic Data Generation role, and why are they important?

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 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 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 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.
More about Synthetic Data Generation jobs
What cities are hiring for Synthetic Data Generation jobs? Cities with the most Synthetic Data Generation job openings:
What states have the most Synthetic Data Generation jobs? States with the most job openings for Synthetic Data Generation jobs include:
What job categories do people searching Synthetic Data Generation jobs look for? The top searched job categories for Synthetic Data Generation jobs are:
Synthetic Data Generation jobs near you
Infographic showing various Synthetic Data Generation job openings in the United States as of May 2026, with employment types broken down into 95% Full Time, and 5% Contract. Highlights an 60% In-person, 15% Hybrid, and 25% Remote job distribution, with an average salary of $93,198 per year, or $44.8 per hour.

Synthetic Data Engineer (AI Data/Training)

Hyphen Connect Limited

Seattle, WA • On-site

$130K - $156K/yr

Full-time

Posted 14 days ago

Job description

We are seeking a talented and innovative Synthetic Data Engineer. In this role, you will design and implement domain-specific synthetic data generation pipelines, ensuring high-quality data management for training loops. Your expertise will drive the success of data processing and model training within the organization.
Responsibilities:
  • Design domain-specific synthetic data generation (SDG) pipelines via self-instruct and constitutional prompting.
  • Implement automated quality scoring and de-duplication systems.
  • Manage data pipelines that feed directly into SFT and DPO training loops.

Qualifications:
  • Proven experience building large-scale data pipelines (Airflow, Spark, Ray).
  • Deep knowledge of prompt engineering for data generation.
  • Familiarity with dataset distillation and bias mitigation.

Apply