Principal Applied Research Engineer, Content Authenticity
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What you'll do
- 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 they're looking for
- 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.
- 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/WinML, and other neural processing SDKs.
- Ability to scope ambiguous problems into executable roadmaps, define milestones, and lead development independently.
- Experience influencing product and research roadmaps through technical vision rather than positional authority.
- Strong collaboration and communication skills, with comfort operating in an R&D environment where requirements evolve.
- PhD in Computer Science, Electrical Engineering, or related field (or equivalent experience).
Nice to have
- Research or engineering background in digital forensics, media provenance, or content authenticity.
- Publications, patents, or recognized contributions in synthetic media detection or related areas.
- Experience developing synthetic video detection models in adversarial settings where generation methods shift faster than detection.
- Familiarity with authenticity standards and industry efforts such as C2PA, or participation in standards bodies and benchmark initiatives.
- Experience building evaluation frameworks and datasets for problems where ground truth is expensive or contested.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
NVIDIA has been transforming computer graphics and accelerated computing for more than 25 years. In the AI era, it’s a unique legacy of innovation that’s fueled by great technology and amazing people. NVIDIA AI for Media is a developer platform for creating and deploying AI features for media and entertainment workflows (https://developer.nvidia.com/topics/ai/generative-ai/ai-for-media). NVIDIA AI for Media (AI4M) provides state-of-the-art AI models for video/audio enhancement and augmented reality features. Built on the NVIDIA AI platform, AI for Media enables developers to deliver studio‑quality audio and high‑resolution video enhancement and effects for live and post-production workflows—from local to cloud.
We are now looking for outstanding engineers to join the NVIDIA AI for Media team. You will work alongside brilliant engineers on core technologies to solve ambitious computer vision and deep learning problems, especially building and optimizing real-time AI solutions that could run anywhere on cloud or premise.
What You'll Be Doing:
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.
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/WinML, and other neural processing SDKs.
Ability to scope ambiguous problems into executable roadmaps, define milestones, and lead development independently.
Experience influencing product and research roadmaps through technical vision rather than positional authority.
Strong collaboration and communication skills, with comfort operating in an R&D environment where requirements evolve.
PhD in Computer Science, Electrical Engineering, or related field (or equivalent experience).
Ways to Stand Out From the Crowd:
Research or engineering background in digital forensics, media provenance, or content authenticity.
Publications, patents, or recognized contributions in synthetic media detection or related areas.
Experience developing synthetic video detection models in adversarial settings where generation methods shift faster than detection.
Familiarity with authenticity standards and industry efforts such as C2PA, or participation in standards bodies and benchmark initiatives.
Experience building evaluation frameworks and datasets for problems where ground truth is expensive or contested.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. As a part of NVIDIA, we have the opportunity to influence the future with your vision and expertise. Are you creative? We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Company
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