Reality Defender
Cybersecurity
StaffAIEngineer(Audio)
Neural analysis suggests this role is
optimal for Senior candidates.
“Staff AI Engineer (Audio) at Reality Defender. Skills: Audio deepfake detection, ML/DL models, Client-facing role. Build audio deepfake detection models. Tune audio deepfake detection models”
Industry & Context.
Investigate failure cases; Implement mitigation strategies; Identify degradation trends; Identify data drift; Identify emerging threat patterns
What They're Looking For.
Must Have
PhD in Computer Science, Machine Learning, Signal Processing, or related field (or equivalent industry experience), 2+ years of industry experience building and deploying ML/DL models for AI products, Programming skills in Python, Proficiency in model ML frameworks (PyTorch, JAX), Solid understanding of audio processing fundamentals, Solid understanding of classification and detection metrics (ROC, DET curves, precision/recall trade-offs), Solid understanding of model robustness and evaluation, Proven experience with large-scale training and benchmarking pipelines
Nice to Have
Prior experience in a client-facing or solutions-oriented AI role is a plus, Comfortable with EST work hours
What You'll Do.
Build audio deepfake detection models
Tune audio deepfake detection models
Deploy audio deepfake detection models
Ensure model robustness
Ensure model reliability
Ensure model performance
Understand client production environments
Define model performance criteria
Investigate failure cases
Build custom evaluation frameworks
Implement mitigation strategies
Design experimentation roadmaps
Align roadmaps with client requirements
Align roadmaps with system resilience goals
Translate findings into actionable insights
Monitor model performance
Measure model performance
Report on model performance
Identify degradation trends
Identify emerging threat patterns
Maintain analytics pipelines
Deploy production-grade models
How You'll Work.
Team & Collaboration
Cross-functional collaboration; Client collaboration; Collaboration with Research; Collaboration with Production Engineering; Collaboration with Customer Success; Collaboration with Applied Scientists; Collaboration with Deployment Engineers; Collaboration with Product teams
Communication Scope
Actionable insights
Process & Methodology
Experimentation roadmaps
Full Job Description
WHO WE ARE. Reality Defender is an award-winning cybersecurity company helping enterprises and governments detect deepfakes and AI-generated media. Utilizing a patented multi-model approach, Reality Defender is robust against the bleeding edge of generative platforms producing video, audio, imagery, and text media. Reality Defender's API-first deepfake detection platform empowers teams and developers alike to identify fraud, disinformation campaigns, and harmful deepfakes in real time. Backed by world class investors including DCVC, Illuminate Financial, Y Combinator, Booz Allen Hamilton, IBM, Accenture, Rackhouse, and Argon VC, Reality Defender works with leading enterprise clients, financial institutions, and governments in order to ensure AI-generated media is not used for malicious purposes. Youtube: Reality Defender Wins RSA Most Innovative Startup https://www.youtube.com/watch?v=TKOZmwyNUNM STAFF AI ENGINEER (AUDIO) We are seeking a Staff AI Engineer (Audio) to build, tune, and deploy state-of-the-art audio deepfake detection models in real-world client environments. This is a highly cross-functional client-facing role requiring close collaboration with Research, Production Engineering, and Customer Success teams. You will be responsible not just for model development, but for ensuring robustness, reliability, and performance under diverse real-world test conditions — including adversarial and edge-case scenarios. This role requires deep hands-on expertise in model building, training, benchmarking, and productionization — along with the ability to translate complex model behavior into actionable insights for both technical and non-technical stakeholders. WHAT YOU'LL DO: - Design, build, and optimize ML/DL models for production-scale audio deepfake detection, ensuring robustness across diverse real-world conditions including compression artifacts, noise, telephony, and streaming pipelines. - Partner with clients to develop a deep understanding of their producti
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