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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.

poolside

Member of Engineering (Synthetic Data Research)

$117K - $140K/yr

Staying in sync with the latest state-of-the-art research in synthetic data generation and LLM training is key to success in this role. You will constantly lead original research initiatives through ...

NT Concepts

Data Scientist

Herndon, VA · On-site

$106K - $180K/yr

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 ...

Propio

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 ...

Saransh Inc

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 ...

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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 Jul 5, 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 the salary of a synthetic data engineer?

The salary of a synthetic data engineer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in data modeling, machine learning, and programming languages like Python or SQL tend to earn higher salaries.

Which 3 jobs will survive AI?

Synthetic Data Generation specialists are likely to continue being in demand as AI development requires high-quality, labeled data for training models. Roles involving data curation, domain expertise, and oversight of AI systems—such as data scientists, AI ethics officers, and machine learning engineers—are also expected to persist due to their specialized skills and the need for human judgment. These jobs often require technical knowledge, programming skills, and continuous learning to adapt to evolving AI technologies.

What is an example of synthetic data generation?

Synthetic data generation, relevant to roles like data scientists or AI engineers, involves creating artificial data that mimics real datasets using algorithms such as generative adversarial networks (GANs) or statistical models. For example, generating realistic customer transaction records for testing machine learning models without exposing sensitive information. This process helps improve model training while maintaining data privacy and security.

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.

Is 40 too late for data science?

Age is not a barrier to entering data science or synthetic data generation roles. Many professionals successfully transition into these fields later in life by acquiring relevant skills such as programming, statistics, and machine learning, often through online courses or certifications. Experience, continuous learning, and adaptability are valued more than age in the tech industry.
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 June 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 66% Physical, 2% Hybrid, and 32% Remote job distribution, with an average salary of $93,198 per year, or $44.8 per hour.

Senior Scientist, Synthetic Data and Privacy (New York)

NVIDIA AI

Manhattan, NY • On-site

Full-time

Posted 4 days ago

Job description

NVIDIA is at the forefront of the AI revolution, and our research is shaping the future of large language models. We are looking for a Senior Scientist to join our team and help advance our capabilities in generating synthetic data and privacy-preserving AI. You will contribute to open-source libraries within the NVIDIA NeMo ecosystem that enable high-quality synthetic data generation and data privacy at scale, including context‑aware anonymization. This role combines hands‑on software engineering with applied research in LLMs and privacy‑enhancing methods, and you will collaborate with research, engineering, product teams, and external labs.

What You’ll Be Doing
  • Build LLM-based methods for synthetic data generation, privacy, and context-aware anonymization, with automated evaluation across multilingual text, documents, and multimodal content.
  • Optimize task-specific LLMs for low‑latency, high‑throughput inference (distillation, quantization), and scale our frameworks to run in real time.
  • Design and maintain open-source libraries and SDKs with clean APIs and strong documentation.
  • Drive software excellence with modern tooling, architecture based on configuration, and professional Git/CI‑CD.
  • Publish original research at top machine learning and AI conferences to maintain NVIDIA‘s technical leadership.
  • Mentor interns and junior researchers to develop technical growth within the team.
What We Need To See
  • PhD in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience.
  • A research background of 2+ years in applied LLM/NLP research and engineering, synthetic data generation, anonymization and PII detection, or related areas. Comparable experience is also considered.
  • Proven track record of developing or maintaining software libraries used by a broad developer community.
  • Strong publication record at premier venues such as NeurIPS, ICML, ICLR, ACL or similar.
Ways To Stand Out From The Crowd
  • Active contributions to open‑source projects, particularly in ML, security, or privacy domains.
  • Deep technical understanding of LLMs and inference optimization (quantization, distillation, latency/throughput tuning), with frameworks such as vLLM or TGI.
  • Ability to build and optimize scalable data processing pipelines for large‑scale models.
  • Functional knowledge of global privacy regulations such as GDPR or CCPA.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 264,500 USD for Level 3, and 192,000 USD - 304,750 USD for Level 4. You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until June 14, 2026.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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