Jobs at webAI

Designs and scales secure infrastructure for AI workloads across multi-cloud, hybrid, and edge environments. Builds Infrastructure as Code and GitOps workflows, operates Kubernetes clusters for AI/ML workloads, develops secure CI/CD pipelines, supports MLOps automation, and implements observability, access management, compliance, incident response, and disaster recovery. The role also includes infrastructure documentation and mentoring junior and mid-level engineers.

Serve as a technical advisor for strategic industrial accounts across manufacturing, energy, and supply chain sectors. Develop AI solution roadmaps, architect edge deployments, lead technical workshops and proofs of concept, assess feasibility and retrofit strategies, and coordinate solution architects, engineers, product teams, sales, and delivery partners. Translate executive business goals and operational requirements into scalable AI, automation, and infrastructure solutions while driving customer adoption and long-term value.

Design and implement secure, reliable synchronization and networking systems for distributed AI across edge devices, cloud environments, and air-gapped government systems. Architect frameworks using content-addressable storage, Merkle trees, and CRDTs; optimize distributed software under resource constraints; apply threat modeling and cryptography; collaborate on restricted-system deployments; and maintain testing, observability, and compliance readiness. The role requires 25–50% travel to offices, government field sites, and test locations.

Build and maintain reusable platform capabilities using Rust and systems-engineering practices. Design stable APIs and SDKs, develop backend software, improve CI/CD and release automation, package and distribute software, and diagnose performance, concurrency, reliability, and integration issues. The role owns features from architecture through production support, creates documentation and developer tooling, and collaborates across product and engineering teams.

Design and implement secure, distributed synchronization and networking systems for AI operations across edge, cloud, isolated, and air-gapped environments. Develop CRDT-based frameworks using content-addressable storage and Merkle trees, optimize distributed software under constrained connectivity, and integrate cryptographic security practices. Collaborate with MLOps, infrastructure, and security teams while maintaining rigorous testing, observability, and reliability standards. Development is primarily in Rust.

Design, automate, secure, and operate infrastructure for AI workloads across multi-cloud, hybrid, and edge environments. Build infrastructure as code, Kubernetes platforms, CI/CD pipelines, MLOps automation, observability systems, and cloud security controls. Participate in incident response, reliability improvements, disaster recovery, and documentation while mentoring other engineers.

Creates polished, on-brand visual designs for web, mobile, digital marketing, and go-to-market channels. Scales campaign identities across web pages, decks, social, email, events, and sales collateral; builds reusable templates; maintains brand assets; and ensures flawless layout, imagery, and typography. Collaborates with design, marketing, writing, and technical partners, works from evolving inputs, and improves creative workflows using AI and template tooling.

Lead messaging, positioning, and go-to-market for commercial and public sector audiences. Translate technical AI capabilities into clear narratives, build demos and sales enablement assets, drive product launches, and partner across product, engineering, sales, and BD to accelerate pipeline and influence roadmap.

The Senior Machine Learning Engineer will design and deploy AI systems, optimize production models, and ensure efficient performance in resource-constrained environments.

The Director of Applied AI leads the development and deployment of AI solutions for clients, manages cross-functional teams, and drives innovation in distributed AI infrastructure.

Lead the development of AI systems integrating computer vision and language understanding, focusing on reinforcement learning and multimodal AI architectures.

The AI Research Scientist will design, train, and optimize state-of-the-art machine learning models, conduct research on model efficiency, and collaborate with engineering teams to ensure real-world impact.

As an R&D Software Engineer, you'll rapidly prototype AI technologies, translating theoretical research into practical implementations while collaborating with cross-functional teams.

The Senior Forward Deployed AI Engineer will create, deploy, and maintain AI systems, optimize workflows, and communicate with customers to address needs within production environments.

Provide high-level administrative and operational support to three C-level executives, managing calendars, travel, meetings, documentation, and cross-functional coordination. Track projects, contracts, expenses, onboarding, and event logistics while maintaining confidentiality and supporting finance, legal, and operations.

Design and implement secure, resilient synchronization and networking systems for distributed AI across edge, cloud, isolated, and air-gapped environments. Build frameworks using content-addressable storage, Merkle trees, CRDTs, and erasure coding. Optimize distributed software under resource constraints and intermittent connectivity, apply threat modeling and cryptography, collaborate with infrastructure and security teams, and maintain rigorous testing, observability, and validation practices.

Lead development and optimization of LLMs and Mixture-of-Experts models, drive research for performance and efficiency (quantization, on-device inference, RAG, agents), integrate models into product, mentor engineers, and deliver cross-functional initiatives.

The AI Research Scientist will develop novel algorithms, lead experimental design, and mentor engineers while focusing on privacy-preserving AI solutions.

The AI Software Engineer will deploy models, build data pipelines, optimize performance for AI models, and develop testing suites in collaboration with AI scientists.