Principal Applied Research Engineer, Content Authenticity
Core
Set technical direction and build real-time AI models for detecting synthetic and manipulated media (content authenticity) across video, audio, and visual modalities.
Role type
Principal Applied Research Engineer (Content Authenticity)
Builds
End-to-end forensics pipelines, detection models, and evaluation benchmarks for media authenticity.
Domain
AI for Media, Computer Vision, Deep Learning, Digital Forensics
Deliverable
production ML models
Required skills
Deep learning architecture, computer vision, model optimization for real-time performance, data strategy, technical roadmap planning, cross-modal analysis (video/audio), system design from research to production
Preferred skills
Digital forensics, media provenance, adversarial detection, standards participation (C2PA), evaluation framework design
Technologies
PyTorch, TensorFlow, ONNX, TensorRT, Triton, WinML, Neural Processing SDKs
Responsibilities
Architect model and pipeline strategy for synthetic media detection; Design and optimize AI models for computer vision and video AI; Extend authenticity coverage to audio and other modalities; Establish benchmarks and failure-mode analysis; Manage accuracy/latency/throughput tradeoffs; Partner with Research and Product teams; Mentor engineers and act as technical authority
Seniority
Principal, strategy & mentorship