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Synthetic Data Jobs (NOW HIRING)

Nvidia

Build synthetic data generation pipelines using LLM-based methods and automated quality evaluation, producing datasets that improve the pre- and post-training of LLMs such as Nemotron - reasoning ...

poolside

Member of Engineering (Synthetic Data Research)

$117K - $140K/yr

  • Medical

  • PTO

This role particularly focuses on generating synthetic data at scale and determining the best strategies to leverage such data into training large models. You'll closely collaborate with other teams ...

NT Concepts

Data Scientist

Herndon, VA · On-site +1

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 ...

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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.

Research Engineer, Synthetic Data

Human Union Data, Inc

San Francisco, CA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 2 days ago

Job description

About HUD
HUD is building infrastructure to create RL training data and evals for frontier AI agents, as well as a marketplace to sell these to frontier labs through the HUD marketplace. Our platform is used by frontier labs, Fortune 500 companies, and startups. We've raised $16M from top VCs and were YC W25.
About the role
We're looking for Research Engineers to build our synthetic data pipeline. You'll turn domain-specific workflows into synthetic training tasks that are realistic enough to enough to teach useful behavior, structured enough to generate at scale, and difficult enough to expand model capabilities.
Responsibilities
  • Work with subject-matter experts to create synthetic tasks for training AI agents across a range of professional and technical domains
  • Design synthetic task generation methods that produce diverse, realistic, and learnable tasks
  • Build systems and tooling to mutate, validate, and improve synthetic tasks
  • Analyze model and agent performance on synthetic tasks to understand what the tasks are teaching and where they fail
  • Develop metrics to quantify and understand synthetic task diversity, realism, learnability, etc.
Experience
You may be a good fit if you have:
  • Proficiency in Python, Docker, and Linux environments
  • Have experience with synthetic data research methods - please elaborate in your application
  • Strong understanding of what "good synthetic data" means and its limitations
  • Built synthetic data pipelines end-to-end without a fully prescribed roadmap
  • Experience working on environments, evals, and benchmarks

Strong candidates may also:
  • Be detail-oriented and able to spot subtle inconsistencies or edge cases in synthetic data
  • Be able to reason from first principles about task design, scoring, and failure modes
  • Thrive in unstructured problem spaces
  • Early-stage startup experience with ability to work independently in fast-paced environments
  • Strong communication skills for remote collaboration across time zones

We prioritize technical aptitude and learning potential over years of experience. Motivated candidates are encouraged to apply even if they don't meet all criteria.
Team & company details
  • Team Size: ~15 people currently, mostly full-time in-person, but some remote.
  • Our team: Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), serial AI startup founders, and researchers with publications at ICLR, NeurIPS, etc.
  • Company stage: We have 8 figures in funding and high revenue growth. We're scaling profitably and quickly to meet very strong demand.
Logistics
  • Employment: Full-time.
  • Location: On-site only, for now. You can join the team in the San Francisco Bay Area or Singapore offices.
  • Visa Sponsorship: We provide support for relocation and visas for strong full-time candidates to the US or Singapore.
  • Timeline: Applications are rolling. The process is 2 technical interviews and a 2-3 day work trial.
What we offer
  • Competitive compensation
  • 100% covered top-of-the-line medical, dental, and vision from Blue Shield of CA (US employees)
  • Lunch and dinner when you're in the office
  • Company-wide holiday break (Christmas Eve to New Year's Day) on top of PTO and paid holidays
  • Other perks including an Equinox membership, 401k, and commuter benefits (US employees)
  • Unlimited* access to tokens for ChatGPT, Claude Code, Cursor, etc. *By unlimited, we mean no one on our token usage leaderboard has ever hit a limit. So we have no idea what the limit is.

Due to high volume, we may not actively respond to every application, but feel free to contact us at recruiting@hud.so or elsewhere if we missed your application!

Apply