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WebAI

Modular secure edge AI platform

Staff Platform Engineer

Platform EngineeringConfirmed live in the last 24 hours
Full-timeAustin, TX, USAIn-person

About the Job

Requirements

  • At least 8 years of experience building and operating production infrastructure, including some bare-metal or on-premises work rather than exclusively managed cloud.
  • Deep experience with at least one workload orchestrator, such as Nomad or Kubernetes, and the judgment to know when not to use one.
  • Strong Terraform and configuration-management skills, such as Ansible, including clean state and environment management across staging and production.
  • Production experience with AWS, Azure, or both, including identity and IAM, KMS, container registries, and managed Kubernetes.
  • Solid networking fundamentals, including WireGuard, Tailscale, or other VPN overlays; firewalls; NAT; load balancers; L4/L7 proxies such as Envoy; DNS; and TLS.
  • Experience with secrets and identity systems such as Vault or an equivalent, PKI/mTLS, and OIDC.
  • CI/CD and release-engineering experience in GitLab, including container builds, artifact signing, and reproducible pinned builds.
  • Observability experience with Prometheus, Grafana, Loki, or equivalents; alert design; and on-call operations.
  • Proficiency in at least one systems language, Rust or Go, and scripting with Python or Bash.
  • Ability to write clear technical runbooks, design documents, and RCAs that others can act on.

Responsibilities

  • Own deployment for the webAI kernel and B2B SDK, delivering single-machine installs, multi-node clusters, and AWS or Azure/AKS cloud installs from one model while keeping cloud and bare-metal deployments closely aligned.
  • Choose and evolve the orchestration approach, such as Nomad, Kubernetes, or configuration-managed native services such as Ansible plus launchd, prioritizing simplicity and reducing custom infrastructure code where possible.
  • Operate Apple-silicon fleets in production, including launchd services, pf firewalling, Lima/Docker virtual machines, GPU and unified-memory sizing for local LLM inference, and recovery after power loss or hardware failure.
  • Build the inference-serving layer with the AI team, including an OpenAI-compatible model gateway, conversation-pinned load balancing, routing by model and persona, and performance/load testing for throughput, time to first token, and concurrency.
  • Own the software supply chain, including GitLab CI, container and OCI model artifacts, signing with cosign or minisign, pinned dependencies, mirrored registries for air-gapped sites, and the release process.
  • Secure the platform with secrets management using Vault, KMS, or cloud secret stores; PKI and mTLS; enterprise identity integration with Entra ID/OIDC and SCIM; and network policy for on-premises sites.
  • Build observability and audit capabilities with metrics, logs, and alerting using Prometheus, Loki, and Grafana, and export audit data and telemetry into customer monitoring tools.
  • Define high availability, set reliability levels and SLOs that can be committed to in customer contracts, and implement them.
  • Write runbooks, recipes, and RCAs so forward-deployed engineers and customers can operate the stack without the role holder, and lead incident response when they cannot.
  • Work with kernel engineers on networking, including QUIC/peer-to-peer connectivity, relays, and NAT traversal, and make the kernel easy to deploy.
  • Mentor engineers and raise the team's standards for infrastructure-as-code, testing, and operations.

Desired Qualifications

  • Experience administering macOS fleets at scale, including launchd, pf, MDM-managed or locked-down environments, and FileVault constraints.
  • Experience running local LLM inference with llama.cpp, MLX, or vLLM; GPU/unified-memory capacity planning; and model packaging.
  • Experience delivering software to air-gapped or disconnected environments, including mirrored registries and offline installers.
  • Experience with peer-to-peer or QUIC networking, such as iroh or libp2p, relays, and NAT traversal.
  • Experience writing Kubernetes operators or controllers, or Nomad tooling such as kube-rs or controller-runtime.
  • Customer-facing deployment experience, including installing and supporting software at customer sites.

About the Company

WebAI

Modular secure edge AI platform

WebAI provides a modular, secure Deep Learning Platform designed for research, distributed deployment, and enterprise AI, optimized for edge computing with limited connectivity. Clients collect, train, and deploy neural networks autonomously through a subscription-based suite of AI tools, with optional custom model development and deployment support. It differentiates itself with edge-friendly operation, low data requirements, robust security, and a modular architecture that easily integrates with existing systems. Its goal is to make advanced AI accessible to a broad audience while delivering reliable, enterprise-grade solutions for industries like healthcare, finance, and manufacturing.

Company size
51-200
Company stage
Series A
Total funding
$77M
Headquarters
Austin, TX
Founded
2020

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