Software Engineer II, AI Developer Tools
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Requirements
• 2+ years building backend systems, APIs, or developer-facing tools with strong software engineering fundamentals • Proficiency in Go (preferred), Rust, Java, or Python with understanding of data structures, algorithms, and design patterns • Basic understanding of AI/ML concepts with eagerness to learn about LLM APIs, prompt engineering, and AI agent development through hands-on work • Experience with cloud platforms (AWS, GCP, or Azure) and understanding of distributed systems or microservices • Familiarity with CI/CD pipelines, automated testing, version control (Git), and modern development workflows • Strong problem-solving skills with ability to work through technical challenges with guidance from senior engineers • Good communication skills in remote, asynchronous environments with ability to document technical decisions • Collaborative mindset with eagerness to learn from code reviews and feedback • Self-motivated with ability to work autonomously while knowing when to ask for help • Passion for developer tools and user experience • Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience • Internship or project experience with AI/ML technologies, LLM APIs, or chatbots • Exposure to AI agent frameworks (LangChain, LangGraph, CrewAI) or similar tools • Experience with developer productivity tools, DevOps practices, or platform engineering • Contributions to open source projects or personal projects involving AI tools • Familiarity with Kubernetes, Docker, or container technologies • Knowledge of infrastructure-as-code tools (Terraform, Pulumi) or GitOps patterns • Understanding of observability tools (Prometheus, Grafana) and monitoring best practices • Computer Science degree or equivalent technical education • Complete onboarding and get up to speed on Docker's AI Developer Tools vision, team mission, and current Agent Dev project • Meet your team including Senior Manager, Principal Engineer, Senior Engineers, and fellow engineers; understand team dynamics and collaboration patterns • Learn about Docker's developer tooling landscape including deployment systems, observability platforms, CI/CD pipelines, and existing infrastructure • Understand Docker's LLM provider relationships, AI technology choices, and integration patterns through documentation and conversations with team members • Set up your development environment and make your first code contributions through bug fixes, documentation improvements, or small feature additions • Participate in code reviews, design discussions, and team meetings to learn technical standards and decision-making processes • Begin learning about AI/LLM technologies through self-study, pair programming with senior engineers, and hands-on experimentation • Take ownership of and deliver your first meaningful feature or component (e.g., specific AI agent capability, LLM integration module, or platform infrastructure improvement) • Contribute regularly to the AI Developer Tools codebase with increasing independence and complexity of contributions • Participate actively in code reviews both as author and reviewer; demonstrate understanding of team coding standards and best practices • Establish monitoring and basic instrumentation for features you've shipped with support from team members • Collaborate with product and design teams to understand feature requirements and user needs • Begin exploring more complex AI/LLM concepts through hands-on implementation work • Support team activities such as sprint planning, retrospectives, and design discussions with thoughtful contributions • One Year Outlook • Own significant features or components of AI developer tools with responsibility for implementation, testing, and basic operational support • Ship multiple AI agent features or platform improvements with demonstrated quality and reliability • Develop strong proficiency in AI/LLM integration patterns, prompt engineering, and agent development through hands-on experience • Contribute meaningfully to technical discussions and design decisions with growing expertise • Help onboard and support newer team members as you gain experience • Participate in team's success delivering developer productivity improvements and supporting productization efforts • Position yourself for growth toward Senior Engineer level through demonstrated technical excellence and increasing scope of ownership • Docker does not offer visa sponsorship for this role.
Responsibilities
• Build AI Developer Tool Features: Implement features for AI-powered developer tools such as code review assistants, test generators, deployment diagnostics, and on-call assistance tools • Implement LLM Integrations: Build integrations with LLM APIs (OpenAI, Anthropic, etc.) such as prompt engineering, response handling, error management, and performance optimization • Contribute to Platform Infrastructure: Help build self-service platform capabilities such as deployment pipelines, observability integration, security controls, and operational tooling that enable teams to rapidly deploy AI developer tools • Support AI-Native Development Adoption: Contribute to tools and programs that help teams adopt AI developer tools such as Claude Code, Cursor, and Warp across Docker's engineering organization • Write Quality Code: Develop well-tested code with unit and integration tests; follow team coding standards and participate actively in code reviews to learn best practices • Maintain Production Systems: Assist with monitoring, alerting, and troubleshooting production AI systems; participate in incident response and learn operational best practices • Collaborate and Learn: Work closely with Senior Engineers and Principal Engineer on technical designs; ask questions, seek feedback, and continuously improve your skills in AI/LLM technologies and platform engineering • Document Your Work: Create clear technical documentation for features you build; contribute to team knowledge base and help future team members understand systems • Participate in Team Activities: Engage in design discussions, sprint planning, retrospectives, and team activities; contribute ideas for improving developer tools and team processes • Grow Your Expertise: Continuously learn about AI/ML technologies, developer tooling best practices, and platform engineering patterns through hands-on work and mentorship from experienced engineers
Benefits
• Freedom & flexibility; fit your work around your life • Designated quarterly Whaleness Days plus end of year Whaleness break • 16 weeks of paid Parental leave • Technology stipend equivalent to $100 net/month • PTO plan that encourages you to take time to do the things you enjoy • Training stipend for conferences, courses and classes • Equity; we are a growing start-up and want all employees to have a share in the success of the company • Medical benefits, retirement and holidays vary by country • Remote-first culture, with offices in Seattle and Paris • Docker embraces diversity and equal opportunity. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better our company will be.