Principal Applied Research Engineer, Content Authenticity

What you'll do

• Architect authenticity models and end-to-end forensics pipelines, including data strategy, evaluation, and deployment.
• Optimize models for accuracy, latency, and throughput on NVIDIA hardware, from research prototypes to real-time production.
• Partner with research and product teams, establish technical roadmaps, and mentor engineers.

What they ask for

• 15+ years of relevant deep learning and computer vision engineering or research experience, including setting technical direction across teams or projects.
• Track record owning a system or model family from research concept through production deployment at scale.
• Hands-on experience with deep learning frameworks and deployment stacks such as PyTorch/TensorFlow/ONNX and TensorRT/Triton/WinML.
• PhD in Computer Science, Electrical Engineering, or related field (or equivalent experience).

Nice to have

• Background in digital forensics, media provenance, or content authenticity; publications, patents, or recognized contributions in synthetic media detection.
• Experience with adversarial synthetic video detection, authenticity standards such as C2PA, or evaluation frameworks and datasets for contested ground truth.

Pay

272,000 USD - 431,250 USD base salary

Tools

PyTorch, TensorFlow, ONNX, TensorRT, Triton, WinML

AI focus

Develops computer vision and video AI for synthetic content detection, manipulation identification, semantic plausibility analysis, and audio authentication; includes evaluation and real-time deployment.