Docker seeks to build AI-powered developer tools to transform how developers write code, debug issues, deploy applications, and respond to incidents, both internally and for customers worldwide.
Requirements
- 3-5 years building production-grade backend systems or developer-facing tools with strong software engineering fundamentals
- Hands-on production experience with AI/ML technologies including practical experience with LLM APIs (OpenAI, Anthropic, etc.), prompt engineering, and AI agent development
- Proficiency in Go (preferred), Rust, Java, or Python with strong software engineering fundamentals
- Experience designing and building distributed systems, microservices, or platform infrastructure
- Strong understanding of cloud-native systems (AWS, GCP, or Azure), APIs, and data stores
- Solid grasp of CI/CD, automated testing, code review practices, and modern development workflows
- Contributions to open source AI tools, developer tooling, or platform engineering projects
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 agents that simplify on-call tasks when handling incidents
- 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 and Senior Engineers on architecture, work with product and design teams on features and UX, and collaborate with platform teams (Infrastructure, Security, Data) on integrations; build effective partnerships across multiple teams
Other
- Ability to work independently on day-to-day work with general guidance on new projects
- Product-minded approach to building developer tools with focus on user experience and measurable outcomes
- Excellent communication skills in remote, asynchronous environments with ability to document technical decisions clearly
- Ability to build effective working relationships across multiple teams
- Ownership mentality with bias for action and iterative delivery
