Principal Applied Research Engineer, Content Authenticity

What you'll do

• Architect the model and pipeline strategy for detecting synthetic and manipulated media.
• Design and build efficient AI models for computer vision and video AI.
• Extend authenticity coverage across multiple modalities, including audio authentication analysis.
• Architect end-to-end forensics pipelines from data strategy through deployment.
• Optimize models for real-time production performance on NVIDIA hardware.
• Translate state-of-the-art research into production-ready AI solutions.
• Act as the technical authority by mentoring engineers and reviewing designs.

What we're looking for

• 15+ years of relevant engineering or research experience in deep learning and computer vision.
• 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, and WinML.
• Ability to scope ambiguous problems into executable roadmaps and lead development independently.
• Experience influencing product and research roadmaps through technical vision.
• PhD in Computer Science, Electrical Engineering, or related field (or equivalent experience).
• Research or engineering background in digital forensics, media provenance, or content authenticity (preferred).