Software Engineer, Agent Harness

About Cartesia

Our mission is to architect AI that learns from and interacts with the world like humans do. We're pioneering the model architectures that will make this possible. Our founding team met as PhDs at the Stanford AI Lab, where we invented State Space Models (SSMs), a new primitive for training efficient, large-scale foundation models.

About the Role

Cartesia builds real-time multimodal intelligence: fast, state-of-the-art voice models, and the platform that puts them into production. In this role, you'll build the platform behind Managed Agents, our voice agents product. You'll own the stack end-to-end and directly shape the latency, reliability, and developer experience of every agent built on Cartesia.

Your Impact

• Evolve the realtime transport layer that carries audio between users and agents, expanding how customers connect across web, SDKs, and telephony without regressing latency or quality.
• Trace and cut end-to-end conversational latency across the STT, LLM, and TTS pipeline.
• Make agent deployments fast and resilient: faster cold starts, smarter scaling, graceful behavior under load.
• Define how voice agent quality is measured, building eval suites, LLM-as-judge scoring, and regression tracking that catch quality and latency regressions before they ship.

What You Bring

• Experience building and operating distributed backend systems in production, ideally at a high-growth startup.
• Strong product instincts: thinking in customer-facing latency, reliability, and developer experience.
• Fluent use of coding agents and a habit of keeping pace with the frontier.
• Clear communication with technical and non-technical audiences, sharp specs, and a strong presence in design reviews.

Nice-to-Haves

• Experience with real-time media systems in production, including WebRTC, SIP, RTP, or low-latency audio.
• Experience with voice agents or agent frameworks such as Pipecat, LiveKit, OpenAI Agents SDK, Claude SDK, or Vercel AI SDK.
• Experience building developer-facing platforms or infrastructure, such as SDK backends or API platforms.
• Experience with distributed systems at scale, including high-throughput workloads, autoscaling, multi-region architectures, or queue-based systems.