Software Engineer II - Cloud-Native Agent Harness (Mecatl)

Accountabilities:: Design, develop, and deliver features across the cloud-native agent harness, including agent execution loops, tool integrations, permission systems, session management, and Kubernetes-based runtime components. Contribute to client-facing interfaces and developer tools, including gRPC, HTTP/SSE APIs, TypeScript SDKs, and command-line applications. Improve platform reliability, security, performance, and observability to ensure production-ready agent infrastructure. Write clean, maintainable, testable code with appropriate error handling and edge-case coverage. Produce technical documentation and design proposals, participate in code reviews, and provide constructive feedback on contributions, including AI-generated code. Take ownership of assigned projects, translating product requirements into technical solutions and delivering improvements independently. Integrate AI coding agents into daily development activities, critically evaluating their output and ensuring code quality, security, and reliability. Experiment with AI-assisted engineering workflows, identify opportunities for improvement, and share practical findings with the wider team. Apply sound security practices when working with AI systems, particularly around permissions, secrets management, untrusted inputs, and agent autonomy. Collaborate with engineering and product stakeholders while contributing to the open-source community and supporting developers using the platform. Requirements: Professional proficiency in Go, with experience writing clean, maintainable, and testable code. Demonstrated ability to independently develop, test, and deliver features within backend, platform, or infrastructure-focused software projects. Working knowledge of distributed systems and cloud-native technologies, particularly Kubernetes, containers, and service APIs such as gRPC or HTTP. Familiarity with concurrency, streaming architectures, durable state management, or distributed application development is an advantage. Hands-on experience using AI coding agents in software development, with the ability to explain their practical benefits, limitations, and associated risks, including prompt injection and excessive agent permissions. Strong interest in AI agent infrastructure, including agent execution frameworks, tool integrations, Model Context Protocol (MCP), model providers, and evaluation systems. Previous experience contributing to open-source projects or developing AI platforms is an advantage. A product-oriented engineering mindset, with curiosity about user needs, the ability to challenge assumptions, and a focus on delivering practical solutions. An AI-first approach to software development, using AI tools thoughtfully to accelerate engineering processes and improve the quality of delivered products. Excellent written and verbal communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences. Strong problem-solving and analytical abilities, combined with a proactive approach to identifying and resolving challenges. Self-motivation, adaptability, and the ability to work independently in a fast-paced environment characterized by evolving priorities and technical challenges. A collaborative mindset and willingness to contribute to team development, knowledge sharing, and continuous improvement. Benefits: Competitive salary: Anticipated annual base salary range of USD $157,500–$192,500. Equity opportunities: Additional equity compensation as part of the overall package. Comprehensive healthcare: Medical, dental, and vision insurance coverage. Remote flexibility: Flexible remote or hybrid working arrangements. Paid time off: Flexible paid time off and paid holidays. Parental support: Paid parental leave. Team collaboration: Company offsites and opportunities to connect and collaborate in person. Professional growth: Opportunities to work alongside experienced professionals in open-source technologies, cloud-native infrastructure, cybersecurity, and AI development. Technical ownership: Freedom to make meaningful engineering decisions and contribute to foundational AI infrastructure. Innovation-driven environment: An AI-first culture that encourages experimentation, continuous learning, and the responsible adoption of emerging technologies. Open-source community: Opportunities to contribute to impactful projects and collaborate with developers and communities worldwide.