Senior Software Engineer, AI Developer Tools

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:

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  Design, build, and operate production AI agents for developer productivity, including code review assistants, test generators, deployment diagnostics, and incident response automation

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  Integrate LLM providers, agent orchestration frameworks, and MCP servers into reliable tools, following the team's patterns and standards

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  Build systems that help Docker's engineers adopt AI-native development tools

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  Build integrations between AI developer tools and existing developer infrastructure such as CI/CD pipelines, observability platforms, and deployment systems

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  Instrument your tools so the team can measure adoption, effectiveness, and developer satisfaction

• 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

• 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

• 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

• 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

• 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)

• 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

• 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

• 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

• 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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