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Synthetic Data Jobs (NOW HIRING)

$180 - $320/hr

Synthetic Data Generation & Evaluation * Design and build scalable synthetic data generation, transformation, filtering, and evaluation pipelines for complex AI use cases * Translate model objectives ...

New

$150 - $200/hr

Reporting to the VP, AI and Intelligence Products, the Senior Director will establish a roadmap for synthetic data offerings that expand the value of Dynata's data assets, unlock new research ...

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 ...

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 ...

Reporting to theVP,AI and Intelligence Products,the Senior Director willestablisha roadmap for synthetic data offerings that expand the value ofDynata'sdata assets, unlock new research methodologies ...

$130 - $170/hr

This role ensures PI/PHI‑compliant synthetic data pipelines, model‑training readiness, system stability, and data availability by working w/IS architecture to keep the mini-arch ahead of major ...

Showing results 21-40

Synthetic Data information

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.

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 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 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.

More about Synthetic Data jobs

What cities are hiring for Synthetic Data jobs?

Cities with the most Synthetic Data job openings:

What states have the most Synthetic Data jobs?

States with the most job openings for Synthetic Data jobs include:

Infographic showing various Synthetic Data job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

Sr. Simulation & Synthetic Data Engineer

Intuitive

Sunnyvale, CA • On-site

Full-time

Re-posted 3 days ago


Job description

Company Description
It started with a simple idea: what if surgery could be less invasive and recovery less painful? Nearly 30 years later, that question still fuels everything we do at Intuitive. As a global leader in robotic-assisted surgery and minimally invasive care, our technologies-like the da Vinci surgical system and Ion-have transformed how care is delivered for millions of patients worldwide.
We're a team of engineers, clinicians, and innovators united by one purpose: to make surgery smarter, safer, and more human. Every day, our work helps care teams perform with greater precision and patients recover faster, improving outcomes around the world.
The problems we solve demand creativity, rigor, and collaboration. The work is challenging, but deeply meaningful-because every improvement we make has the potential to change a life.
If you're ready to contribute to something bigger than yourself and help transform the future of healthcare, you'll find your purpose here.
Job Description
About the Team
Future Forward Research - Synthetic Data, builds the simulation and data infrastructure that powers Intuitive's autonomous surgical capabilities.
The Role
You'll build the virtual surgical worlds and the data pipelines that train our perception and policy models. Day to day, that means designing simulation environments, generating large volumes of labeled synthetic data, and working with ML engineers to close the sim-to-real gap for robotic surgery. It's a hands-on engineering role at the intersection of 3D simulation, machine learning, and robotics.
What You'll Do
  • Design and build high-fidelity simulation environments for surgical tasks.
  • Generate scalable synthetic datasets including photo realistic imagery, segmentation masks, depth, optical flow, and kinematic state - and own their quality, versioning, and delivery.
  • Implement domain randomization and procedural scene generation to maximize sim-to-real transfer.
  • Model deformable soft-tissue physics, tool-tissue contact, and instrument kinematics that match Intuitive's platforms.
  • Partner with ML engineers to define task curricula, reward functions, and evaluation benchmarks.
  • Develop and refine sim-to-real transfer strategies, and validate simulated behaviors against benchtop phantoms and real systems.
  • Build reusable tools, APIs, and documentation so the broader team can spin up new tasks without deep simulation expertise.

Qualifications
What You Bring
We care more about real depth in a few of these than shallow coverage of all of them.
  • Strong software engineering. You write maintainable, tested code in Python, C++ and/or C#, and you're comfortable in a Linux / Git / Docker / GPU workflow.
  • Hands-on simulation or graphics experience. You've built things in a physics simulator, game engine, or rendering/VFX pipeline - robotics simulators, real-time engines, and offline graphics pipelines all count.
  • A feel for data and models. You've produced data that trained an ML model (or worked closely alongside that), and you understand how data quality and distribution show up in model behavior.
  • Working knowledge of 3D and physics fundamentals - coordinate frames, rendering, rigid-body and contact dynamics - enough to reason about why a simulated scene does or doesn't look and behave correctly.

You've likely spent several years building games, simulation, graphics, robotics, or data systems - but we're far more interested in what you've built than in a specific number on your résumé. If you don't check every box, we still want to hear from you. Strong candidates often come from autonomous driving, humanoid robotics, gaming, or VFX rather than surgical robotics specifically.
Education
  • Bachelor's degree in Computer Science, Computer Graphics, Robotics, Electrical or Mechanical Engineering, Physics, or a related technical field - or equivalent practical experience.
  • An advanced degree (MS or PhD) in a related area is a plus, not a requirement. We've hired strong engineers from all paths, including self-taught backgrounds and industry work in games, VFX, or robotics.

Bonus Points
None of these are required - they're signals, not gates. Any one of them is a nice plus.
  • Deformable-object simulation: FEM, position-based dynamics, or differentiable physics
  • NVIDIA Isaac Sim/Lab
  • Surgical robotics simulation: ORBIT-Surgical, dVRK-based environments
  • Sim-to-real techniques: domain randomization, system identification, privileged learning, residual policies
  • Vision-language or vision-language-action models, and how simulation data supports them
  • Procedural/generative asset creation: NeRFs, Gaussian Splatting, or diffusion models
  • Distributed compute at scale: Ray, Kubernetes, Slurm, multi-GPU/multi-node
  • Medical imaging or surgical video pipelines
  • Publications or open-source work in simulation, graphics, robotics, or AI

Tools You Might Use
You won't touch all of these, and we don't expect you to walk in knowing them.
Category
Examples
Simulation engines
Unity, NVIDIA Isaac Sim/Lab, MuJoCo
Deformable physics
PhysX FEM, SOFA, DiSECt, NVIDIA Warp/Newton, PBD
Rendering & data
USD, Omniverse Replicator, Blender
Infrastructure
Docker, Kubernetes, Ray, Airflow, CUDA
What Success Looks Like in Your First Year
  • Simulation environments for several core surgical tasks are operational and integrated with the ML training pipeline.
  • Synthetic data delivers measurable model lift on targeted perception or policy metrics.
  • A quantitative sim-to-real correlation is established on benchtop phantoms.
  • Parallelized infrastructure reliably generates thousands of training episodes per hour with automated dataset delivery.
  • A reusable task-creation framework lets ML engineers define new surgical skills without deep simulation expertise.

Shift
  • Day

Workplace Type
  • Onsite - This job is fully onsite in the Sunnyvale campus.

Additional Information
Due to the nature of our business and the role, please note that Intuitive and/or your customer(s) may require that you show current proof of vaccination against certain diseases including COVID-19. Details can vary by role.
Intuitive is an Equal Opportunity Employer. We provide equal employment opportunities to all qualified applicants and employees, and prohibit discrimination and harassment of any type, without regard to race, sex, pregnancy, sexual orientation, gender identity, national origin, color, age, religion, protected veteran or disability status, genetic information or any other status protected under federal, state, or local applicable laws.
Mandatory Notices
U.S. Export Controls Disclaimer: In accordance with the U.S. Export Administration Regulations (15 CFR §743.13(b)), some roles at Intuitive Surgical may be subject to U.S. export controls for prospective employeeswho are nationals from countries currently on embargo or sanctions status.
Certain information you provide as part of the application will be used for purposes of determining whether Intuitive Surgical will need to (i) obtain an export license from the U.S. Government on your behalf (note: the government's licensing process can take 3 to 6+ months) or (ii) implement a Technology Control Plan ("TCP") (note: typically adds 2 weeks to the hiring process).
For any Intuitive role subject to export controls, final offers are contingent upon obtaining an approved export license and/or an executed TCP prior to the prospective employee'sstart date, which may or may not be flexible, and within a timeframe that does not unreasonably impede the hiring need. If applicable, candidates will be notified and instructed on any requirements for these purposes.
We will consider for employment qualified applicants with arrest and conviction records in accordance with fair chance laws.
Preference will be given to qualified candidates who do not reside, or plan to reside, in Alabama, Arkansas, Delaware, Florida, Indiana, Iowa, Louisiana, Maryland, Mississippi, Missouri, Oklahoma, Pennsylvania, South Carolina, or Tennessee.
This position may be filled at a different job level than listed here depending on
business need and/or on the selected candidate's experience, knowledge and skills.
Compensation will be based primarily on the job level at which the role is filled and the
candidate's qualifications, consistent with applicable law.
We provide market-competitive compensation packages, inclusive of base pay, incentives, benefits, and equity. It would not be typical for someone to be hired at the top end of range for the role, as actual pay will be determined based on several factors, including experience, skills, and qualifications. The target compensation ranges are listed.