About the job
We're building the infrastructure that lets people talk to Claude—real-time, bidirectional voice conversations that feel natural, responsive, and safe. This is foundational work for how millions of people will interact with AI. The Voice Platform team designs and operates the serving systems, streaming pipelines, and APIs that bring Anthropic's audio models from research into production across Claude.ai, our mobile apps, and the Anthropic API.
Responsibilities
Design and build the real-time streaming infrastructure that powers voice conversations with Claude—ingesting microphone audio, orchestrating model inference, and streaming synthesized speech back with minimal latency
Build low-latency serving systems for speech models, optimizing time-to-first-audio and end-to-end conversational responsiveness
Develop the public and internal APIs that expose voice capabilities to Claude.ai, mobile clients, and third-party developers
Own the audio transport layer—codecs, jitter buffers, adaptive bitrate, packet loss recovery—so conversations stay smooth across unreliable networks
Build observability and quality-measurement systems for voice: latency distributions, audio quality metrics, interruption handling, and turn-taking accuracy
Partner with Audio research to move new model architectures from experiment to production, and feed real-world performance data back into research
Collaborate with mobile and product engineering on client-side audio capture, playback, and the end-to-end user experience
Qualifications
Minimum
Have 6+ years of experience building distributed systems, real-time infrastructure, or platform services at scale
Have shipped production systems where latency is measured in tens of milliseconds and users notice when you miss
Preferred
Real-time media protocols and stacks: WebRTC, RTP, gRPC bidirectional streaming, or WebSockets at scale
Audio engineering fundamentals: codecs (Opus, AAC), voice activity detection, echo cancellation, jitter buffering, or audio DSP
Low-latency ML inference serving, streaming model outputs, or GPU-based serving infrastructure
Telephony, live streaming, video conferencing, or voice assistant platforms
Mobile audio pipelines on iOS (AVAudioEngine, AudioUnits) or Android (Oboe, AAudio)
Working alongside ML researchers to productionize models—speech experience is a plus but not required