Location requirements
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 Software Engineer to join our new AI Developer Tools team building the future of AI-powered developer productivity. This is an exciting opportunity to work on 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.
You'll work at the intersection of AI and developer experience, contributing to production systems that leverage LLMs and AI agents to accelerate developer workflows. You'll help build 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. As these tools mature and demonstrate value, you'll participate in transforming them into commercial offerings for Docker's customers
This is a hands-on execution role where you'll collaborate closely with experienced engineers, learn rapidly about AI/LLM technologies, and ship production features in a fast-paced, remote-first environment that values rapid iteration and continuous learning.
You're excited about AI and its potential to transform developer productivity. You may be early in your career with AI/ML technologies, but you're eager to learn about LLMs, prompt engineering, and AI agents through hands-on work. You have solid software engineering fundamentals and experience building backend systems or APIs. You're comfortable with ambiguity, enjoy experimenting with new technologies, and learn quickly through iteration. You take ownership of your work, communicate clearly in remote environments, and actively seek feedback to improve. You think about user experience and care about building tools that developers love to use. Most importantly, you're collaborative, ask great questions, and thrive in a supportive team environment where continuous learning is encouraged.
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
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
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
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
Demonstrate measurable growth in technical skills around AI technologies, platform engineering, and developer tools
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
We use Covey as part of our hiring and / or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 13, 2024.
Please see the independent bias audit report covering our use of Covey here.
Perks
Freedom & flexibility; fit your work around your life
Designated quarterly Whaleness Days plus end of year Whaleness break
Home office setup; we want you comfortable while you work
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
Docker Swag
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.
Due to the remote nature of this role, we are unable to provide visa sponsorship.
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Docker is the world's leading software container platform. Developers use Docker to eliminate "works on my machine" problems when collaborating on code with co-workers. Operators use Docker to run and manage apps side-by-side in isolated containers to get better compute density. Enterprises use Docker to build agile software delivery pipelines to ship new features faster, more securely and with confidence for both Linux and Windows Server apps.
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