Principal Applied Research Engineer, Content Authenticity

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.
• What We Need To See
• 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.

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