Staff Platform Engineer

About the role

About Us:

webAI is pioneering the future of artificial intelligence by establishing the first distributed AI infrastructure dedicated to personalized AI. We recognize the evolving demands of a data-driven society for scalability and flexibility, and we firmly believe that the future of AI lies in distributed processing at the edge, bringing computation closer to the source of data generation. Our mission is to build a future where a company's valuable data and intellectual property remain entirely private, enabling the deployment of large-scale AI models directly on standard consumer hardware without compromising the information embedded within those models. We are developing an end-to-end platform that is secure, scalable, and fully under the control of our users, empowering enterprises with AI that understands their unique business. We are a team driven by truth, ownership, tenacity, and humility, and we seek individuals who resonate with these core values and are passionate about shaping the next generation of AI.

About the Role:

webAI runs private AI on the customer's own hardware: on-prem Apple-silicon fleets (some air-gapped), Linux servers, and the customer's own cloud. As Staff Platform/Infrastructure Engineer you'll own the substrate that makes this repeatable. That means how our kernel, services and local model inference get packaged, deployed, secured, observed and upgraded across bare metal and cloud.

You'll inherit a production deployment serving a global enterprise customer, lead its move to a leaner next-generation architecture, and turn it into a deployment kit our forward-deployed engineers can use to install a new customer from the docs alone. This is a hands-on staff role: you'll make the architecture calls, write the code and carry the pager.

What You'll Do:

- Own deployment for the webAI kernel and B2B SDK. Deliver single-machine installs, multi-node clusters, and cloud (AWS, Azure/AKS) installs from one model, keeping the gap between cloud and bare metal small.

- Choose and evolve our orchestration approach (Nomad, Kubernetes, or config-managed native services such as Ansible plus launchd) with simplicity as the goal. Cut custom infrastructure code where you can.

- Run Apple-silicon fleets in production: launchd services, firewalling (pf), Lima/Docker VMs, GPU/unified-memory sizing for local LLM inference, and recovery after power loss or hardware failure.

- Build the inference serving layer with the AI team: an OpenAI-compatible model gateway, conversation-pinned load balancing, routing per model and per persona, and performance/load testing (throughput, time to first token, concurrency).

- Own the software supply chain: GitLab CI, container and OCI model artifacts, signing (cosign/minisign), pinned dependencies, mirrored registries for air-gapped sites, and the release process.

- Secure the platform: secrets management (Vault, KMS, cloud secret stores), PKI and mTLS, enterprise identity integration (Entra ID/OIDC, SCIM), and network policy for on-prem sites.