webAI
webAI builds an end-to-end platform for training, deploying, and operating private AI models on infrastructure customers control, including edge devices. It sells enterprise software and developer SDKs/APIs used by public-sector, defense, aviation, and manufacturing teams to run specialized models with data sovereignty. Founded in 2020 and headquartered in Austin, Texas, the privately held company focuses on distributed AI/LLM infrastructure across local and cloud environments.
Open Positions at webAI
13 open positions
Staff Platform / Infrastructure Engineer owning the deployment, security, observability, and reliability substrate for webAI’s private AI platform across customer-owned hardware, bare metal, and cloud environments. This hands-on staff role combines architecture, coding, operations, incident response, and mentorship.
Senior DevOps Engineer will design and operate secure, scalable infrastructure for AI workloads across multi-cloud, hybrid, and edge environments. The role is a high-impact individual contributor focused on infrastructure automation, Kubernetes, platform reliability, security, and MLOps enablement.
Forward Deployed Engineer building secure, resilient peer-to-peer networking and data synchronization systems for distributed AI across edge, cloud, and restricted government environments. The role centers on Rust-based backend systems, decentralized networking, and deployments in air-gapped settings.
Senior Rust Platform Engineer building reusable, high-performance platform capabilities, APIs, SDKs, and developer tooling for webAI’s distributed edge AI infrastructure. The role owns systems from architecture through release and production support, with a strong focus on reliability, compatibility, and developer experience.
Build secure, resilient CRDT-based synchronization and networking systems for distributed AI infrastructure spanning edge devices and isolated cloud environments. The role focuses on decentralized and peer-to-peer technologies, with development primarily in Rust.
Jr DevOps Engineer responsible for building secure, scalable infrastructure for AI workloads across cloud, hybrid, and edge environments. The role focuses on automation, Kubernetes, CI/CD, observability, reliability, and MLOps infrastructure.
Build secure, resilient peer-to-peer infrastructure that synchronizes and operates AI workloads across edge devices, cloud systems, and restricted environments. This role focuses on decentralized networking, distributed systems, and privacy-preserving data synchronization using Rust and backend systems languages.
Lead the development, optimization, and deployment of advanced Large Language Models and Mixture of Experts models for a private, enterprise-focused AI platform. This staff-level role combines hands-on machine learning engineering, model research, technical leadership, and mentoring.
Build high-performance AI software that turns machine learning models into scalable, reliable production systems for edge devices and cloud environments. This role combines low-level systems programming, ML inference, distributed computing, and hardware-aware optimization.
Senior Machine Learning Engineer building production-ready AI systems for secure, distributed, and resource-constrained environments. The role focuses on productionizing models, optimizing inference across cloud and edge hardware, and developing agentic and retrieval-augmented AI systems.
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