Docker - Senior Software Engineer, AI Developer Tools
Requirements
• 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.
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 • 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
Benefits
• United States: $160,900 – $260,700 + equity • United States: • ______________________________________________________________________ • Open vacancy: This posting is for an existing open role. • Open vacancy: • AI in hiring: Docker may use AI-assisted tools during our recruiting process. • AI in hiring: • 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. • Interview recordings: • Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration. • Remote-first by design • Flexibility that fits your life – We trust you to manage your schedule while delivering great work. • Flexibility that fits your life • Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break. • Time to recharge • Technology stipend – Equivalent to US$100 net per month to help support your work. • Technology stipend • US$100 net per month • Learning & development – Annual stipend for conferences, courses, certifications, and continued learning. • Learning & development • Parental leave – 16 weeks of paid parental leave after six months of employment. • Parental leave • Equity for all full-time employees – Share in Docker's long-term success as we continue to grow. • Equity for all full-time employees • Comprehensive benefits – Medical, retirement, and paid holidays vary by country. • Docker swag – Because representing the whale never gets old. • Docker swag
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