Weave was founded to build the robots we’d want to have in our own home. We believe the next generation of robotics will transform everyday life by enabling people to do more and to reclaim time to spend on what’s important.
We also believe robots are in a sense like any other product: to matter, they have to ship. Our robots are already operating in real homes and businesses, giving us the opportunity to rapidly improve from real-world experience. With a growing team, strong customer demand, and capital for expansion, we’re entering an exciting stage of growth—and we’re looking for people with exceptional talent and standards to help bring home robotics to millions of households.
Responsibilities
Cloud inference, teleoperation, and the companion app all run on infrastructure you'll own. As a Cloud Infrastructure Engineer, you'll build the networking stack that connects robots to GPUs, teleoperators, and customers’ companion apps. You’ll design and implement protocols that keep those connections working on imperfect home networks, and the backend that manages a growing fleet across many customers.
The core problems are real-time: routing a robot to available GPU capacity with latency low enough for closed-loop inference, keeping teleoperation streams stable through jitter and packet loss, and degrading gracefully when the network does. What you build directly sets how responsive every robot in the fleet feels day to day.
Build our distributed infra: Design backend services that coordinate robots, cloud compute, teleoperators, and customer applications across unreliable networks and partial failures. Build for fault tolerance, idempotency, consistency, scalability, and graceful degradation as the fleet grows.
Serve cloud inference: Build low-latency serving infrastructure for models robots call mid-task, including connection management, GPU scheduling and utilization, autoscaling, load balancing, and graceful degradation when connectivity or compute capacity is constrained.
Own schema evolution, migrations, performance, backup, and recovery as these systems scale.
Secure robot-to-cloud communication: Build authentication, authorization, device identity, credential provisioning and rotation, and secure communication between robots and cloud services, partnering with Security from architecture through deployment.
Build the real-time transport stack: Own low-latency media and control transport end-to-end: connection lifecycle, NAT traversal on real home networks, encrypted transport, congestion control, jitter handling, and the latency budget teleoperation depends on.
Fleet telemetry. Ingestion, storage, and query of logs and metrics from every robot in the field, serving engineering, fleet operations, and ML data needs.
Own production reliability: Define SLOs and build monitoring, alerting, deployment, and incident-response systems that keep critical fleet services available and make failures diagnosable.
What You'll Bring
3+ years building backend or media infrastructure in production, with real ownership of systems under load.
Real-time networking depth. You've built or operated low-latency streaming systems over UDP, debugged NAT traversal failures across home routers, reasoned about congestion control, and read protocol source when documentation ran out; you didn't just call an SDK.
Cloud infrastructure knowledge: Experience operating production cloud infrastructure using infrastructure-as-code and containerization tools such as Terraform/OpenTofu, Ansible, Docker, and Kubernetes, with hands-on experience deploying, monitoring, and debugging services in production.
Networking fluency: Deep understanding of UDP/TCP, NAT traversal, STUN/TURN/ICE, congestion control, packet loss, jitter, and the realities of consumer Wi-Fi and home networking.
Cloud & device security: Familiarity with TLS/mTLS, PKI, certificates, KMS/HSMs, secrets management, device identity, and credential provisioning and rotation for connected devices or distributed systems.
Production data experience: schema design, migrations, and operational care for databases customers depend on.
Requirements
Media pipeline knowledge: codecs, rate control, jitter buffers, and where quality goes to die.
Contributions to aiortc, Pion, LiveKit, or similar RTC projects.
Inference serving or robotics/IoT fleet infrastructure experience.
Experience with GPU infrastructure, model serving, inference scheduling, or other high-performance compute workloads.