About Docker

Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world’s largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout.

We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default.

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Docker is looking for a Senior Software Engineer to build AI-powered developer tools and the platform behind them on our AI Developer Tools team. You will work at the intersection of AI, developer experience, and platform engineering, building tools that make Docker’s engineers more productive and the platform that lets teams across Docker prototype, deploy, and scale their own AI developer tools.

As a Senior Engineer you will design, build, and operate:

AI-Powered Developer Tools. AI agents and tools that accelerate developer productivity, surface observability insights, and automate operational reviews. You will help teams adopt an AI-native way of working and speed up adoption of tools such as Claude Code and Codex. Examples include AI-powered code review and refactoring assistants, automated test generators and local environment setup tools, deployment pipeline diagnostic agents, and agents that simplify on-call work during incidents.

Self-Service AI Developer Tools Platform. Platform infrastructure that lets product and platform teams across Docker unblock themselves by prototyping, building, and deploying their own AI developer tools, using the patterns and standards the team sets.

You will report to the Senior Manager, AI Developer Tools, and work closely with the team’s Principal and Staff engineers, product engineering teams, and platform teams across Docker.

What Would Make Someone Successful in This Role

You are a strong, hands-on engineer who enjoys building at the intersection of AI, developer experience, and platform engineering. You have built software on top of LLMs and AI agents and have a good sense of what works in practice. You care about the people using your tools, and you can tell a tool developers love from one they put up with. You take a feature from a vague request to production: you ask the right questions about requirements, design it sensibly, ship it iteratively, and operate it afterward. You are comfortable in a fast-moving AI landscape and balance quality with pragmatism. You make the engineers around you better through thoughtful code review and by sharing what you learn.

Responsibilities

Building and Operating AI Developer Tools

• Design, build, and operate production AI agents for developer productivity, including code review assistants, test generators, deployment diagnostics, and incident response automation
• Integrate LLM providers, agent orchestration frameworks, and MCP servers into reliable tools, following the team’s patterns and standards
• Build systems that help Docker’s engineers adopt AI-native development tools
• Build integrations between AI developer tools and existing developer infrastructure such as CI/CD pipelines, observability platforms, and deployment systems
• Instrument your tools so the team can measure adoption, effectiveness, and developer satisfaction

Self-Service Platform

• Build and improve platform capabilities, including deployment pipelines (ArgoCD/GitOps), observability integration (Grafana), security controls, and cost management, that help teams ship their own AI tools faster
• Help reduce time-to-production for new products from weeks to days
• Contribute to reliability, security, and performance standards for AI systems, including SLOs, monitoring, and incident response

Design and Delivery

• Take features and services from requirements to production. Ask about scale, users, and constraints before designing, and write down your assumptions
• Write clear design documents for the work you own, and participate in design reviews for the team
• Review…