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Reality Defender is a cybersecurity company specializing in deepfake and AI-generated media detection. The company uses a patented multi-model approach to detect fraudulent video, audio, imagery, and text across enterprise and government clients. Backed by top-tier investors including DCVC, Y Combinator, Booz Allen Hamilton, IBM, and Accenture, Reality Defender has won awards including RSA Most Innovative Startup.
As a Software Engineer II, you will design, develop, and maintain Microsoft Teams bot applications with real-time audio and video capabilities. Key responsibilities include building low-latency media streaming features, developing Windows-based services in C# and .NET, deploying containerized applications on Kubernetes, and integrating with Microsoft APIs and cloud services. You'll diagnose media issues involving codecs, synchronization, and network conditions, create automated tests, troubleshoot production issues using logs and metrics, and collaborate with product and engineering teams to translate requirements into maintainable solutions.
You bring 3–5 years of professional software development experience with strong C#/.NET proficiency and hands-on experience with Windows application development. You have production experience with real-time audio and video streaming systems, containerized deployments on Kubernetes, and troubleshooting Kubernetes workloads. You understand real-time media concepts such as codecs, buffering, latency, and synchronization. You're comfortable with REST APIs, event-driven systems, multithreading, memory management, performance profiling, Git, automated testing, and modern CI/CD practices.
Nice-to-have skills include experience with Microsoft Teams apps, Microsoft Graph, Azure Bot Service, WebRTC, RTP/RTCP, FFmpeg, GStreamer, Azure services, Helm, and operating real-time communications software in production.
Within six months, you'll contribute production-ready features to the Teams bot application, independently diagnose and resolve audio/video streaming problems, deploy and troubleshoot in Kubernetes environments, and improve reliability, test coverage, scalability, and observability of the media pipeline.
