Principal Applied Research Engineer, Content Authenticity

About the job

Responsibilities

• Set technical direction for content authenticity AI, architecting model and pipeline strategy for detecting synthetic and manipulated media.
• Design and build AI models for computer vision and video AI, focusing on synthetic content detection and AI manipulation identification.
• Extend authenticity coverage across modalities, including audio authentication analysis.
• Architect new end-to-end forensics pipelines, from data strategy and evaluation to deployment.
• Own the accuracy/latency/throughput tradeoff space, taking models from research prototype to real-time production performance.
• Partner with NVIDIA Research to bring state-of-the-art work into production.
• Partner with AI4M product teams to shape roadmap based on technical feasibility and field direction.
• Act as the technical authority on media authenticity, mentoring engineers and reviewing designs.

Requirements

• 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 (PyTorch/TensorFlow/ONNX) and deployment stacks (TensorRT/Triton/WinML).
• Ability to scope ambiguous problems into executable roadmaps and lead development independently.
• Experience influencing product and research roadmaps through technical vision.
• Strong collaboration and communication skills, comfortable in an R&D environment.
• PhD in Computer Science, Electrical Engineering, or related field (or equivalent experience).

Benefits

• Equity
• Benefits