About the Role
At Docker, we make app development easier so developers can focus on what matters. Our remote-first team spans the globe, united by a passion for innovation and great developer experiences. With over 20 million monthly users and 20 billion image pulls, Docker is the #1 tool for building, sharing, and running apps—trusted by startups and Fortune 100s alike. We're growing fast and just getting started. Come join us for a whale of a ride!
Docker seeks a Senior Software Engineer to join our new AI Developer Tools team at the forefront of AI-powered developer productivity. You'll build cutting-edge AI agents and tools that transform how developers write code, debug issues, deploy applications, and respond to incidents—both internally at Docker and for our customers worldwide.
This is an opportunity to work at the intersection of AI and developer experience, building production systems that leverage LLMs and AI agents to accelerate developer workflows. You'll architect and implement AI-powered tools such as code review assistants, automated test generators, deployment diagnostics agents, and on-call assistance tools. You'll also contribute to the self-service platform that enables teams across Docker to rapidly build and deploy their own AI developer tools.
Your work will directly impact how Docker's engineers build and operate services powering 20 million users, and as these tools mature, you'll help transform them into commercial offerings for Docker's customers.
You'll collaborate closely with the Principal Engineer on technical architecture, partner with product and design teams on user experience, and work autonomously in a fast-paced, remote-first environment where rapid iteration and shipping are core values.
What Would Make Someone Successful In This Role
You're passionate about developer experience and excited by the potential of AI to transform how developers work. You have hands-on experience building production systems with LLMs or AI agents, and you understand the nuances of prompt engineering, agent orchestration, and evaluating AI system effectiveness. You think in terms of products and platforms, balancing technical excellence with pragmatism to ship iteratively while maintaining high quality bars. You're comfortable navigating the rapidly evolving AI/LLM landscape, experimenting with new tools and approaches, and making pragmatic technology choices. You thrive in ambiguity, enjoy rapid prototyping, and have strong opinions on what makes developer tools that engineers love to use. Most importantly, you're collaborative, communicate clearly in remote environments, and take ownership of your work from design through deployment and operations.
Responsibilities
• Build AI-Powered Developer Tools: Design, implement, and ship production-ready AI agents and tools that accelerate developer productivity such as code review and refactoring assistants, automated test generators, local environment setup tools, deployment pipeline diagnostic agents, and on-call assistance tools
• Implement LLM Integrations: Build robust, production-grade integrations with LLM APIs (OpenAI, Anthropic, etc.) such as prompt engineering, response parsing, error handling, rate limiting, cost management, and performance optimization
• Develop Agent Orchestration Systems: Create agent frameworks and orchestration systems that enable complex multi-step workflows, tool calling, context management, and agent-to-agent communication
• Contribute to Platform Infrastructure: Build self-service platform capabilities that enable teams across Docker to rapidly deploy and operate their own AI developer tools such as deployment pipelines, observability integration, security controls, and operational tooling
• Drive Adoption of AI-Native Development: Build tools and programs that accelerate adoption of AI developer tools such as Claude Code, Cursor, and Warp across Docker's engineering organization
• Ensure Production Quality: Write well-tested code with strong test coverage (unit, integration, end-to-end); establish monitoring, alerting, and operational excellence for AI systems
• Collaborate Cross-Functionally: Partner with Principal Engineer on architecture, work with product and design teams on features and UX, and collaborate with platform teams (Infrastructure, Security, Data) on integrations
• Participate in Operations: Take part in on-call rotation for AI developer tools; respond to incidents, debug production issues, and drive continuous improvement of system reliability
• Mentor and Share Knowledge: Guide other engineers through code reviews, pair programming, and technical discussions; document patterns and best practices for AI tool development
• Measure and Iterate: Instrument AI tools to measure adoption, effectiveness, and developer productivity impact; iterate based on data and user feedback to continuously improve developer experience
• On-Call Rotatation: Take part in on-call rotation for your team; respond to incidents,