> Markdown version of [/jobs/ext/4515914-software-engineer-ai-infrastructure-development-platform](https://www.wearedevelopers.com/jobs/ext/4515914-software-engineer-ai-infrastructure-development-platform). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, AI Infrastructure Development Platform - **Company:** Skydio, Inc. - **Location:** San Mateo, CA, United States - **Experience:** Expert - **Salary:** $190,000 - $250,000 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, C++ (Programming Language), Cloud Computing, Continuous Integration, Programming Tools, Firmware, Python (Programming Language), TypeScript - **Published:** November 1, 2026 - **Apply:** https://startup.jobs/senior-software-engineer-ai-infrastructure-development-platform-skydio-com-8371749 ## About the Role * 5+ years building developer tools, infrastructure, or platform systems. * Hands-on experience with LLM-based tools and agents: you've built with them, not just used them * Strong grounding in Python/Go/TypeScript/C++, CI/CD, and cloud infrastructure AWS/GCP * A product mindset toward internal users: you talk to them, ship quickly, and iterate. * Experience with security-sensitive environments or monorepos ## Description About the role: You'll build the platform that puts AI coding agents into the daily workflow of hundreds of Skydio engineers. You'll work across our codebase, CI, and internal infrastructure to produce secure tools that increase our development velocity. You'll work directly with engineers to find where they lose time and build the tools that get that time back. How you'll make an impact: * Build and extend our agentic coding platform: agent integrations, MCP servers, and sandboxed execution * Design guardrails such as permissions, secrets handling, and auditability, so agents can be trusted with sensitive code. * Build the infrastructure that lets teams quickly deploy internal tools and agents * Embed with teams across firmware, autonomy, cloud, and apps to find workflow bottlenecks and fix them. * Measure impact through adoption, cycle time, and quality, and use that data to set priorities. * Drive adoption: be the internal expert on AI development tools and help engineers get real value from them.cost-efficient.