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 code thoroughly and share what you learn with the rest of the team
Take part in the team's on-call rotation, and help improve alerts, runbooks, and tooling so the next incident is easier
Follow the AI-assisted engineering practices the team sets: use AI tools critically, validate their output, and stay accountable for what you ship
Collaboration and Growth
Work with the Senior Manager and the team's Principal and Staff engineers to understand priorities and shape your work
Partner with Infrastructure, Security, and Data teams on integrations and shared standards
Help newer engineers get up to speed on the team's tools, patterns, and practices
Keep up with emerging AI/LLM technologies and agent frameworks, and bring useful findings back to the team
Qualifications
Required
6+ years of software engineering experience building and operating production systems
Hands-on experience building LLM-powered applications, AI agents, or AI-assisted developer tools
Working knowledge of LLM APIs (OpenAI, Anthropic, and others), prompt engineering, agent orchestration frameworks, and practical uses of AI in software development workflows
Experience building and operating scalable backend services, distributed systems, or developer-facing platforms
Experience with cloud-native infrastructure, including Kubernetes, CI/CD pipelines, and observability systems
Proficiency in Go (preferred), Rust, Java, or Python, with strong software engineering fundamentals
Experience building developer tools, platform systems, or internal tools that other engineers rely on
Good product judgment: you weigh developer experience and technical trade-offs, and you ask clarifying questions before committing to a design
Clear written and spoken communication suited to a remote, async-first company
Ownership mentality with a bias for action and iterative delivery in ambiguous, fast-moving environments
Comfortable working autonomously in a distributed, remote-first team
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
Preferred
Experience with MCP (Model Context Protocol) or similar AI agent integration standards
Background in developer productivity, DevOps, SRE, or platform engineering
Contributions to open source AI tools, developer tooling, or platform engineering projects
Experience with GitOps deployment patterns (ArgoCD) or Grafana-based observability
Familiarity with security and operational practices for production AI systems
Experience with infrastructure-as-code (Terraform, Pulumi) and major cloud platforms (AWS, GCP, Azure)
What to Expect
First 30 Days
Learn Docker's current AI and developer tooling landscape, the team's services, and the architecture decisions made so far
Meet your teammates, including the Principal and Staff engineers, the Senior Manager, and key stakeholders in product engineering
Ship a useful production change in your first weeks, using the team's AI-assisted engineering workflow
Review existing AI tool prototypes and internal experiments to learn what is working and what is not
Shadow on-call and learn the operational profile of the team's services
First 90 Days
Own a feature or service from design to production with measurable impact, such as part of an AI code review agent, a deployment diagnostic agent, or an on-call assistant
Write the design document for work you own and take part in team design reviews
Contribute to the self-service platform's deployment pipeline, observability integration, or security controls
Take part in the on-call rotation
First Year Outlook
Be a go-to engineer for one or more of the team's AI tools or platform components
Have shipped multiple production improvements that Docker engineers use every day, with adoption and effectiveness you can point to
Have helped teams outside AI Developer Tools build and run their own tools on the platform
Have taken on larger and more ambiguous work as your scope grows, and helped newer engineers do the same
Docker considers visa sponsorship on a case-by-case basis based on business needs.
Compensation & Equity
United States: $160,900 – $260,700 + equity
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Posting Information
Open vacancy: This posting is for an existing open role.
AI in hiring: Docker may use AI-assisted tools during our recruiting process.
Interview recordings: Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent.
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Perks & Benefits
Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration.
Flexibility that fits your life – We trust you to manage your schedule while delivering great work.
Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break.
Home office support – Set up your workspace for comfort and success.
Technology stipend – Equivalent to US$100 net per month to help support your work.
Learning & development – Annual stipend for conferences, courses, certifications, and continued learning.
Parental leave – 16 weeks of paid parental leave after six months of employment.
Equity for all full-time employees – Share in Docker's long-term success as we continue to grow.
Comprehensive benefits – Medical, retirement, and paid holidays vary by country.
Docker swag – Because representing the whale never gets old.
Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community.
