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Graphics Simulation and Synthetic Data Engineer
Develops advanced 3D simulations and rendering pipelines to generate synthetic training data for computer vision and machine learning models, particularly for VR and AR applications
Key Responsibilities:
- Design and build advanced 3D simulations in Unreal Engine to generate computer vision and machine learning synthetic data
- Develop rendering features and synthetic data pipeline capabilities that operate at enterprise scale
- Code primarily in C++ and Python for simulation development and data pipeline optimization
- Collaborate with multidisciplinary teams spanning rendering, optics, systems, and algorithm development
- Conduct design and code reviews while driving performance measurement, analysis, and optimization
- Work with technical artists and infrastructure teams to debug performance issues across the technology stack
- Analyze and improve efficiency, scalability, and stability of simulation systems and data generation processes
Skills & Tools:
- Game engines (Unreal Engine, Unity, custom engines)
- Programming languages (C++, Python)
- 3D computer graphics and computer vision frameworks
- Synthetic data generation and ML data pipeline tools
- Performance optimization and debugging platforms
- Version control and collaborative development tools
- Systems analysis and scalability optimization techniques
- Cross-functional project management and communication skills
- Experience with VR/AR development pipelines
Where This Role Has Appeared:
- DigitalFish (Digital Media Technology, San Francisco Bay Area/Remote, $145k-$185k, July 2025)
Variants & Related Titles:
- Synthetic Data Engineer
- Computer Vision Data Engineer
- VR/AR Simulation Engineer
- 3D Graphics Data Engineer
- ML Data Simulation Developer
Why This Role Is New:
Graphics Simulation and Synthetic Data Engineer emerged in 2022-2023 as AI models became increasingly data-hungry and companies discovered that high-quality synthetic data could solve training data scarcity issues, especially for VR/AR applications where real-world data collection is expensive or impractical. The role bridges traditional game development expertise with modern AI/ML data pipeline requirements.
Trend Insight:
As AI models require increasingly diverse and large-scale training datasets, companies are investing heavily in sophisticated synthetic data generation capabilities, creating specialized engineering roles that combine gaming technology with machine learning infrastructure to solve AI's data challenges.
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