Member of Technical Staff (Software Engineer, Multimodal)

We are hiring builders to define how people talk to, show things to, and hear from AI. In 2026, we launched Computer, the defining product for the new era of agentic AI. The Multimodal team builds the experiences and infrastructure that move AI interaction beyond touch and text — realtime voice, vision, and the platform systems behind them. As a backend engineer on Multimodal, you will design and scale the distributed systems that carry live voice sessions in production — and drive entirely new products at the intersection of voice, vision, and agents.

Responsibilities

Design, build, and scale the backend session-worker architecture that powers realtime voice: durable per-session workers, provider routing, and stateful streaming over gRPC.; Own distributed-systems problems end-to-end — session lifecycle, crash recovery, reconnection and replay, multi-region deployment, and graceful degradation under real production load.; Build provider-agnostic streaming protocols from our backend to the Rust SDK that powers voice across every client stack.; Drive new products and initiatives in voice and multimodal AI from problem definition through technical design, implementation, and launch.; Build the orchestration layer that lets live voice models delegate work to tools, agents, and long-running tasks — safely, asynchronously, and at scale.; Partner closely with SDK, client, infrastructure, and model teams; work across the stack when the product demands it, from backend services to client-facing APIs.

Requirements

4+ years of professional software engineering experience building backend or distributed systems.; Strong experience in Rust, Python, or Go (we work primarily in Rust and Python).; Experience designing and operating production distributed systems: streaming RPC (gRPC or similar), stateful services, message-driven architectures, and failure recovery.; Solid understanding of cloud infrastructure — deploying, scaling, and operating services on AWS or equivalent.; Strong product judgment and the ability to translate user problems into simple, effective technical solutions.; Genuine interest and adoption of AI products and willingness to learn quickly.

Nice to have

Experience with realtime media systems: voice, audio streaming, WebRTC, or low-latency transport.; Full-stack range — comfort building end-to-end, from backend services through client SDKs or web frontends.; Experience integrating LLMs, speech models, or computer vision into production systems.; Experience with agent frameworks, tool-calling architectures, or sandboxed execution environments.; Time spent at a fast-growing startup or on a high-ownership engineering team.