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Synthetic Data Jobs (NOW HIRING)

NT Concepts

Data Scientist

Herndon, VA · On-site

$106K - $180K/yr

Generate and analyze synthetic data to augment computer vision models where real-world data is scarce * Train, evaluate, and optimize deep neural network models on overhead imagery, including ...

shrm

AI/Synthetic Engineer

Alexandria, VA · Hybrid

$110K - $135K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

Summary Build the organization's synthetic-audience capability: the data-grounding layer that connects first-party audience data to large language models, the synthetic-audience models themselves ...

Jobtailor

Senior Research Data Scientist

San Diego, CA · On-site

$120 - $180/hr

Own the full data lifecycle for visual tasks: dataset curation, annotation strategy, augmentation, synthetic data generation, and quality analysis. * Collaborate closely with engineering and product ...

SHRM

AI/Synthetic Engineer

Alexandria, VA · Hybrid

$110K - $135K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

OverviewBuild the organization's synthetic-audience capability: the data-grounding layer that connects first-party audience data to large language models, the synthetic-audience models themselves ...

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Synthetic Data information

What are the key skills and qualifications needed to thrive as a synthetic data engineer, and why are they important?

To thrive as a Synthetic Data Engineer, you need a strong background in computer science, statistics, and data modeling, usually with a degree in a related field. Experience with programming languages like Python or R, familiarity with machine learning frameworks, and knowledge of data privacy tools are essential. Strong analytical thinking, attention to detail, and effective communication help in designing robust data solutions and collaborating with stakeholders. These skills ensure the creation of high-quality synthetic datasets that support research, model training, and compliance with data privacy regulations.

What is the difference between Synthetic Data vs Data Analyst?

AspectSynthetic DataData Analyst
CredentialsNone required, but knowledge of data generation tools helpfulBachelor's degree in data science, statistics, or related field
Work EnvironmentData labs, software development teams, AI/ML projectsBusiness environments, analytics teams, reporting platforms
Industry UsageAI training, testing, privacy complianceData interpretation, reporting, decision support

While Synthetic Data involves creating artificial datasets for testing and training AI models, Data Analysts focus on interpreting real-world data to generate insights. Both roles require data literacy, but Synthetic Data specialists focus on data generation techniques, whereas Data Analysts analyze existing data to inform business decisions.

What are the main challenges faced by professionals working with synthetic data in a production environment?

One of the primary challenges in a synthetic data role is ensuring that the generated datasets accurately reflect real-world scenarios while maintaining privacy and compliance standards. Professionals often need to balance data utility with the risk of introducing bias or unrealistic patterns. Collaboration with data scientists, engineers, and domain experts is essential to validate results and integrate synthetic data into machine learning pipelines. Additionally, staying updated on evolving tools and best practices is crucial for maintaining data quality and relevance.

What is synthetic data and how is it used?

Synthetic data refers to artificially generated information that mimics real-world data but does not contain any actual personal or sensitive details. It is commonly used to train machine learning models, test software, and protect privacy when sharing datasets. By using synthetic data, organizations can avoid data privacy concerns and still gain valuable insights or test algorithms effectively. This approach is especially valuable in industries like healthcare and finance where real data may be restricted. Synthetic data can be generated using various statistical techniques, simulations, or machine learning models.
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Synthetic Data jobs near you
Infographic showing various Synthetic Data job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution.

Senior Software Engineer, Perception ML Data

Nuro

Mountain View, CA • On-site

$66.25 - $87.75/hr

Full-time

Re-posted 10 days ago

Job description

Job Summary:
Nuro is a company focused on advancing autonomy performance through a team of high-output generalists where ML and systems engineering converge. The Perception ML Data Engineer will bridge machine learning innovation and autonomy infrastructure, ensuring models learn from relevant, diverse, and high-quality data to enhance autonomous systems' understanding and adaptability.
Responsibilities:
• Design and advance systems that leverage VLMs to curate geographically diverse datasets matching real-world driving distributions
• Develop high fidelity synthetic data frameworks across sensor modalities
• Optimize ML-powered validation of data quality and model readiness
• Architect hybrid systems combining deep learning and classical algorithms for scalable data curation and annotation
• Design frameworks to quantify synthetic data’s real-world fidelity and improve synthetic data rendering quality
• Build tools that automatically surface data gaps impacting perception model performance
• Collaborate with autonomy engineers to turn raw sensor streams into targeted training priorities – addressing critical gaps that limit perception and autonomy performance
Qualifications:
Required:
• BS in Computer Science, Robotics, Statistics, Physics, Math or another quantitative area.
• 4+ years of industry software engineering experience with Python fluency and C/C++ familiarity.
• Proven ability to lead cross-functional technical projects from design to completion.
• Practical experience in implementing ML solutions and enjoy integrating them into real-world systems.
• Focus on deploying impactful, integrated solutions rather than purely theoretical ML experimentation.
Preferred:
• Familiarity working with synthetic or autonomous driving data.
• Experience building ML systems for robotic applications.
Company:
Nuro is a robotics company specializing in the development of autonomous driving technologies. Founded in 2016, the company is headquartered in Mountain View, USA, with a team of 501-1000 employees. The company is currently Late Stage.

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