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.

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

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

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.

• 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.

#LI-REMOTE