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Synthetic Data Generation Jobs (NOW HIRING)

AI Engineer

Leawood, KS · On-site

$111K - $133K/yr

Support post-training data workflows such as SFT, instruction tuning, preference data, RLHF/DPO-style data, reward model data, and synthetic data generation. * Use modern annotation tools and AWS ...

AI Engineer

Leawood, KS · On-site

$111K - $133K/yr

Support post-training data workflows such as SFT, instruction tuning, preference data, RLHF/DPO-style data, reward model data, and synthetic data generation. * Use modern annotation tools and AWS ...

Senior Robotics Data Engineer - Only W2

Warren, MI · On-site

$99K - $135K/yr

... and synthetic data generation. · Manage data versioning, metadata, and dataset governance to support model training, evaluation, and regression testing. · Collaborate with Robotics Perception ...

OR · On-site

$63 - $83/hr

Guide partners on synthetic data generation, scenario coverage, data strategy, and evaluation methods for perception, planning, controls, and embodied AI. * Use and advise on coding agents such as ...

AI Engineer

Leawood, KS · On-site

$111K - $133K/yr

Support post-training data workflows such as SFT, instruction tuning, preference data, RLHF/DPO-style data, reward model data, and synthetic data generation. * Use modern annotation tools and AWS ...

... HIPAA-aligned synthetic data generation. • Partner with PBM Business SMEs, QA teams, AI engineers, and IT teams. Qualifications : Required : • Expertise in LLMs, RAG, vector DBs, cloud ...

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Synthetic Data Generation information

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$31K

$93.2K

$169K

How much do synthetic data generation jobs pay per year?

As of Jun 9, 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:
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.
Language Engineer, Artificial General Intelligence - Data Services

Language Engineer, Artificial General Intelligence - Data Services

Amazon

Boston, MA • On-site

$124K - $149K/yr

Full-time

This job post has expired today. Applications are no longer accepted.


Amazon rating

7.4

Company rating: 7.4 out of 10

Based on 6,828 frontline employees who took The Breakroom Quiz

6th of 39 rated national retailers


Job description

The Amazon Artificial General Intelligence (AGI) Data Services organization is responsible for developing diverse datasets to train and evaluate the Amazon AI models. We are looking for Language Engineers to join our science and engineering team to support the development of complex, multimodal datasets, using a range of approaches including synthetic data generation, model-supported data generation, and human-in-the-loop data collections.
You will play a critical role in driving innovation and advancing the state-of-the-art in evaluating and training AI models.

You will work closely with cross-functional teams, including product managers, engineers, and data scientists to ensure that our AI systems are best in class.
Key job responsibilities
- Design complex data collections with human participants in response to science needs: author instructions, define and implement quality targets and mechanisms, provide day-to-day coordination of data collection efforts (including planning, scheduling, and reporting), and be responsible for the final deliverables
- Design and conduct complex data creation tasks using synthetic and model-based data generation methods, following state-of-the-art approaches
- Analyze and extract insights from large amounts of data
- Build tools or tool prototypes for data analysis or data creation, using Python or another scripting language
- Use modeling tools to bootstrap or test new AI functionalities
- Collaborate with scientists, software engineers, and other data creators to evaluate performance of AI models
About the team
Amazon strives to be the world's most customer-centric company, where customers can research and purchase anything they might want online or offline. We set big goals and are looking for people who can help us reach and exceed them. The AGI organization provides AI capabilities for a variety of Amazon products and searches

We provide secure, flexible, cost effective, and high-quality data development services to our customers, that enables them to build advanced ML models.


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About Amazon

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Amazon.com, Inc., commonly known as Amazon, is an American multinational technology company. It was founded by Jeff Bezos in 1994 and initially started as an online marketplace for books. Since then, Amazon has expanded its operations and become one of the largest e-commerce companies in the world. Amazon's primary business is its online retail platform, where customers can purchase a vast array of products, including electronics, clothing, books, home goods, and much more. The company offers a convenient and user-friendly shopping experience, with features such as fast shipping, customer reviews, and personalized recommendations. In addition to its e-commerce platform, Amazon has diversified its business into various other areas. One of its notable ventures is Amazon Web Services (AWS), a comprehensive cloud computing platform that provides services such as storage, compute power, and database management to individuals and businesses. AWS has become a leader in the cloud computing industry, powering many websites and applications worldwide. Amazon has also developed its own consumer electronics, including the popular Amazon Kindle e-reader, Fire tablets, Fire TV streaming devices, and the Alexa-powered Echo smart speakers. The Alexa voice assistant, integrated into these devices, allows users to interact with their devices using voice commands, perform tasks, and access information. Furthermore, Amazon has expanded into media and entertainment. It operates Prime Video, a streaming service that offers a wide range of movies, TV shows, and original content. Amazon Music provides a platform for streaming and purchasing digital music, while Audible offers audiobooks and other audio content. The company's commitment to customer satisfaction and convenience is demonstrated by its membership program, Amazon Prime. Prime members receive various benefits, including free two-day shipping, access to streaming services, exclusive deals, and more.

Industry

It services, book publishers, retail, real estate and computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Seattle, WA, US