Role overview
We are seeking a Senior Applied AI Engineer 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 work on
- Design, build, and optimize ML/DL models for production-scale audio deepfake detection.
- Interface with clients to build a deep understanding of the production environment and scope out model performance criteria.
- Investigate failure cases in the client environment, build custom evaluation frameworks, and implement mitigation strategies that span across Engineering and AI.
- Drive model tuning and iteration for performance under a variety of real-world environments, e.g. compression artifacts, noise, telephony, and streaming pipelines.
- Design and execute structured experimentation roadmaps, centered around client requirements, anticipated future attack vectors, and proactive system resilience.
- Collaborate with cross-functional partners (Applied Scientists, Deployment Engineers, Product teams) to deploy robust and scalable models.
- Present technical findings and model performance insights to clients as well as internal stakeholders.