AI platforms have infrastructure requirements that general-purpose cloud deployments do not — low-latency inference pipelines, privacy-preserving data flows, and the ability to shift computation between cloud and edge as the platform evolves alongside wearable hardware.
As a Lead Infrastructure Engineer you will own the systems that make all of this possible. You will work directly with the CEO and across the full engineering team — backend, mobile, and AI — to build and operate the infrastructure layer that keeps the platform fast, reliable, secure, and scalable as we grow from a closed beta to a production consumer product.
This is a hands-on role. You will design, build, and operate the infrastructure yourself — not manage a team of engineers doing it. You will be the person the engineering team relies on when deployment pipelines break, latency spikes, or a new AI workload needs to be provisioned correctly. You will also be the person who builds the systems that prevent those problems from happening in the first place.
We actively use AI-assisted development tools across our engineering team — Cursor, Claude, Copilot — and expect engineers who use them seriously as a core part of their workflow.
What You'll Build and Own
CI/CD pipelines — the automated build, test, and deployment infrastructure that lets a distributed engineering team across Silicon Valley, Paris, and Shenzhen ship confidently and quickly
AI workload infrastructure — the compute, networking, and storage configurations that support LLM inference, embedding generation, vector search, and RAG pipelines at low latency and meaningful scale
Kubernetes cluster management — provisioning, scaling, and operating containerized services across cloud environments, with particular attention to the cost and latency tradeoffs of AI workloads
Observability and monitoring — the logging, metrics, alerting, and tracing systems that give the engineering team full visibility into platform behavior in production
Security and compliance infrastructure — the systems that enforce our privacy-by-design architecture, including encrypted data pipelines, secrets management, network security, and access controls
Infrastructure as code — Terraform, Helm, ArgoCD or equivalent, ensuring the entire infrastructure is reproducible, version-controlled, and auditable
Edge and on-device infrastructure planning — as the platform transitions from cloud-first to edge-first over the next 12 to 18 months, you will be the person who designs the infrastructure architecture that supports that transition