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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

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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)

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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

Visa sponsorship: Docker considers visa sponsorship on a case-by-case basis based on business needs.

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Compensation & Equity

All full-time roles include equity. Final compensation is determined on factors including but not limited to experience, skills, qualifications, and location.

United States: $160,900 – $260,700 base

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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.

#LI-REMOTE