Staff AI Engineer Audio

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

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 production environments and define model performance criteria.

Investigate failure cases in client environments, build custom evaluation frameworks, and implement mitigation strategies spanning both Engineering and AI.

Design and execute structured experimentation roadmaps aligned with client requirements and proactive system resilience goals. Translate findings into clear and actionable insights.

Monitor, measure, and report on model performance in production using data analytics and AI observability tools (e.g. Datadog, Metabase). Identify degradation trends, data drift, and emerging threat patterns before they impact client outcomes.

Build and maintain dashboards and analytics pipelines that surface model health metrics, enabling data-driven decisions across AI, Engineering, and Product teams.

Collaborate with cross-functional partners — Applied Scientists, Deployment Engineers, and Product teams — to