Sr. Client Developer - Rust, Network and AI

Summary

We are seeking a senior client-side systems engineer to lead development of secure, high-performance client software for a global zero-trust platform. This role owns network-facing client components, protocol logic, and reliability-critical functionality that operate at enterprise scale. The ideal candidate brings deep Rust expertise, strong networking fundamentals, and an AI-native engineering mindset to accelerate development while maintaining rigor, security, and quality.

Responsibilities

• Design, build, and evolve client-side software in Rust for production deployment
• Implement and maintain network protocol handling, secure communications, and traffic-processing logic
• Own performance, latency, resiliency, and stability across production client software
• Debug complex issues spanning networking, concurrency, OS interaction, and runtime behavior
• Partner with product, security, backend, and platform teams to deliver end-to-end features
• Build clean, testable, observable, production-grade code
• Apply AI-native engineering practices to speed up analysis, code generation, test creation, and troubleshooting
• Contribute to secure software development practices and high-trust engineering standards

Requirements

• Strong hands-on experience with Rust
• Deep understanding of network protocols and client/server communication
• Senior-level experience in systems, infrastructure, or client software engineering
• Strong debugging and problem-solving skills for performance and reliability issues
• Experience building secure, production-quality software
• Comfort working close to the OS, network stack, and low-level runtime behavior
• Strong ownership mindset and ability to drive critical components independently

Nice to Have

• Experience in zero-trust, endpoint, VPN, proxy, or traffic inspection systems
• Familiarity with sockets, TLS, transport protocols, and packet-level behavior
• Experience building agents, SDKs, desktop clients, or enterprise networking software
• Exposure to distributed systems or high-scale infrastructure
• Practical use of AI tools in engineering workflows: code review, test generation, debugging, documentation, or incident analysis