Principal Applied Research Engineer, Content Authenticity
NVIDIA· Engineer, Sys SW
Santa Clara, CA$272k - $431.3kfull-timePosted 1mo ago
About the role
Senior engineering/research role focused on building and deploying content authenticity AI for media, including detection of synthetic/manipulated media, multi-modal forensics pipelines, and production-grade performance on NVIDIA hardware.
Responsibilities
- Set the technical direction for content authenticity AI at NVIDIA — architecting the model and pipeline strategy for detecting synthetic and manipulated media, and making the build-vs-adapt calls that shape multi-year investment.
- Design and build highly capable, efficient AI models for computer vision and video AI across synthetic content detection, AI manipulation identification, and semantic plausibility analysis.
- Extend authenticity coverage across modalities, including audio authentication analysis and more.
- Architect new end-to-end forensics pipelines, from data strategy and evaluation methodology through deployment — establishing the benchmarks and failure-mode analysis the team measures itself against.
- Own the accuracy/latency/throughput tradeoff space, taking models from research prototype to real-time production performance on NVIDIA hardware.
- Partner deeply with NVIDIA Research to bring state-of-the-art work into production, and with AI4M product teams to shape roadmap based on what's technically achievable and where the field is heading.
- Act as the technical authority on media authenticity for the broader org — mentoring engineers, reviewing designs, and representing NVIDIA's work externally where appropriate.
Qualifications
- 15+ years of relevant engineering or research experience in deep learning and computer vision, with a track record of setting technical direction for a research area across multiple teams or projects.
- Demonstrated ownership of a system or model family from research concept through production deployment at scale.
- Hands-on development skills with deep learning frameworks and deployment stacks such as PyTorch/TensorFlow/ONNX, TensorRT/Triton/WinML, and other neural processing SDKs.
- Ability to scope ambiguous problems into executable roadmaps, define milestones, and lead development independently.
- Experience influencing product and research roadmaps through technical vision rather than positional authority.
- Strong collaboration and communication skills, with comfort operating in an R&D environment where requirements evolve.
- PhD in Computer Science, Electrical Engineering, or related field (or equivalent experience).
Skills
Required:Deep learningModel architecture and deploymentPyTorch/TensorFlow/ONNXTensorRT/Triton/WinMLSystem/product roadmap influence
Benefits
- Equity
- Benefits (see NVIDIA benefits)
Last updated Sep 11, 2026. Interested? Apply on the company site.
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