Principal Applied Research Engineer, Content Authenticity

NVIDIA
Santa Clara
Workplace: OnsiteFull timeUSD 272,000 - 431,250 annuallyFunction: Research & Scientific (R&D)Experience: 15+ yearsEducation: phdSkills: ["Collaboration","Communication","Independent leadership","Mentoring","Ownership"]

Set the technical direction for media content authenticity, architecting deep-learning model and pipeline strategies to detect synthetic and manipulated media. Build efficient computer-vision/video AI models spanning video, audio, and semantic plausibility analysis, and design end-to-end forensics pipelines from evaluation through deployment. Own accuracy/latency/throughput tradeoffs on NVIDIA hardware while partnering with Research and AI4M product teams and acting as the org’s media authenticity technical authority.

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FursaFursa
NVIDIA
NVIDIA
3 days ago

Principal Applied Research Engineer, Content Authenticity

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Source: Company careers pageValidated by: Fursa AI
Last checked: 3 hours agoStatus: Live

Job Summary

Set the technical direction for media content authenticity, architecting deep-learning model and pipeline strategies to detect synthetic and manipulated media. Build efficient computer-vision/video AI models spanning video, audio, and semantic plausibility analysis, and design end-to-end forensics pipelines from evaluation through deployment. Own accuracy/latency/throughput tradeoffs on NVIDIA hardware while partnering with Research and AI4M product teams and acting as the org’s media authenticity technical authority.
Location: Santa Clara
Workplace: Onsite
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Mid level

Key Responsibilities

  • •Set the technical direction for content authenticity AI, architecting model and pipeline strategy for detecting synthetic and manipulated media.
  • •Design and build efficient computer vision and video AI models across detection and plausibility analysis, including expanding authenticity coverage to audio.
  • •Architect new end-to-end forensics pipelines from data strategy and evaluation methodology through deployment.
  • •Own accuracy/latency/throughput tradeoffs, taking models from research prototypes to real-time production on NVIDIA hardware.
  • •Partner with NVIDIA Research and AI4M product teams, and act as the technical authority through mentoring, design reviews, and external representation.

Pay and Benefits

Salary: USD 272,000 - 431,250 annually
Equity and Bonus:Equity
Perks:EquityBenefits

Key Requirements

  • •15+ years of relevant engineering or research experience in deep learning and computer vision, with a 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.
  • •PhD in Computer Science, Electrical Engineering, or related field (or equivalent experience).
Experience:15+ yearsDeep learningComputer visionForensicsMedia provenanceSynthetic media detection
Education:PhD / Doctorate in Computer Science, Electrical Engineering, or related field
Skills:CollaborationCommunicationIndependent leadershipMentoringOwnership
Tech Stack:PyTorchTensorFlowONNXTensorRTTritonWinML

Company Brief

NVIDIA
Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.
Industry: Electronics Manufacturing
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Santa Clara, United States
Founded: 1993
Glassdoor
Glassdoor: 4.3
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