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Capacity is an AI-powered support automation platform that helps teams automate support and business processes across omni-channel environments. The company has raised over $100 million from 150+ investors and serves enterprise customers looking to grow revenue, reduce costs, and improve satisfaction metrics.
You will join a specialized team working at the intersection of high-performance AI and confidential computing. This role focuses on architecting secure inference infrastructure using Trusted Execution Environments (TEEs) and cryptographic isolation. Your primary responsibility is designing and implementing the cross-layer integration and microservices architecture that powers Capacity's secure AI platform.
Key responsibilities include: architecting resilient backend endpoints and multi-module microservices; implementing attestation-gated mechanisms to safeguard model weights and execution boundaries within hardware-enforced environments; establishing secure inference pipelines that maintain cryptographic certainty and data isolation; and ensuring the platform meets enterprise security and performance requirements.
You will own technical decisions around TEE integration, secure model deployment, and the infrastructure that enables customers to run powerful AI models with absolute data protection. This is a hands-on engineering role where you'll translate complex security protocols into production systems that scale.
The ideal candidate has deep experience with backend systems, distributed computing, and ideally exposure to confidential computing, cryptographic protocols, or secure enclaves. You should be comfortable working in a fast-moving environment where security and performance are equally critical.
