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 Staff Software Engineer to set the technical direction for key parts of our AI Developer Tools team. You will work at the intersection of AI, developer experience, and platform engineering, building the AI-powered tools and the platform that let teams across Docker prototype, deploy, and scale their own AI developer tools.
As a Staff Engineer you will own the technical design and delivery for two areas:
AI-Powered Developer Tools. Design and build 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. Design and build the platform infrastructure that lets product and platform teams across Docker unblock themselves by prototyping, building, and deploying their own AI developer tools. You will set the patterns and standards that let teams experiment, iterate quickly, and graduate successful prototypes into production-ready services.
You will report to the Senior Manager, AI Developer Tools, and work closely with the team's Principal engineers, engineering leadership, product engineering teams, and platform teams across Docker.
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You are a hands-on technical leader who works well at the intersection of AI, developer experience, and platform engineering. You have shipped production systems built on LLMs and AI agents, and you can explain what worked and what did not. You think in platforms: you design something once so that several teams can move faster. You have clear opinions about what makes a developer tool good, and sound judgment about when to build, when to integrate, and when not to do either. You are comfortable in a fast-moving AI landscape and balance technical quality with shipping iteratively. Before you design, you ask about scale, users, and constraints rather than assuming them. You lead through influence and mentorship, and the engineers around you get better because you are on the team.
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Set technical direction and own the architecture for AI-powered developer tools and the self-service platform within the team's roadmap
Turn ambiguous problems into clear requirements, plans, and staged decisions. Ask about scale, users, and constraints before designing, and write down your assumptions
Establish patterns, standards, and best practices for LLM integration, agent development, and production AI systems that the team and partner teams adopt
Shape platform capabilities including deployment frameworks (ArgoCD/GitOps), observability integration (Grafana), security controls, and operational tooling for AI developer tools
Design reliable, scalable infrastructure for hosting AI agents and developer tools
Recommend AI technology choices, LLM provider strategies, and agent orchestration approaches, and make the trade-offs explicit
Work with the Senior Manager and the team's Principal engineers to align technical decisions with business goals
Design, build, and operate production AI agents for developer productivity, including code review assistants, test generators, deployment diagnostics, and incident response automation
Build self-service platform capabilities that reduce time-to-production for new AI tools from weeks to days
Build systems that speed up adoption of AI-native development tools (Claude Code, Codex) across Docker's engineering organization
Set and meet reliability, security, and performance standards for AI systems, including SLOs, monitoring, incident response, and cost management
Design integrations between AI developer tools and existing developer infrastructure such as CI/CD pipelines, observability platforms, and deployment systems
Take part in the team's on-call rotation and use what you learn to improve alerts, runbooks, and tooling
Evaluate emerging AI/LLM technologies, developer tooling trends, and agent frameworks, and recommend what Docker should adopt and what it should not build
Define how we measure AI tool effectiveness, including adoption, productivity gains, and developer satisfaction
Improve AI tool performance, cost efficiency, and developer experience through architectural improvements
Contribute to the technical approach for hardening proven internal tools so other teams across Docker can adopt them
Lead cross-team technical delivery, surface risks early, and work with Infrastructure, Security, and Data on shared standards
Mentor senior engineers on AI/LLM integration patterns, agent development, and platform engineering practices
Lead design reviews and raise the quality of design and code reviews across the team
Create reusable patterns, documentation, and architectural decision records (ADRs) that other engineers build on
Help the team turn good individual AI-assisted engineering practice into shared workflows and controls
This role may require participation in an on-call rotation to provide support outside of standard business hours, including evenings, weekends, and holidays, as needed.
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8+ years of software engineering experience, with demonstrated technical leadership on production systems at scale
Hands-on production experience building LLM-powered applications, AI agents, or AI-assisted developer tools
Strong understanding of LLM APIs (OpenAI, Anthropic, and others), prompt engineering, agent orchestration frameworks, and practical uses of AI in software development workflows
Track record of designing and building scalable distributed systems and developer-facing platforms
Production experience with cloud-native infrastructure, including Kubernetes, GitOps deployment patterns, observability systems, and CI/CD pipelines
Proficiency in Go (preferred), Rust, Java, or Python, with strong software engineering fundamentals
Experience designing developer tools, platform systems, or internal tools that other teams rely on
Track record of clarifying requirements and constraints with stakeholders before committing to a design
Track record of leading complex initiatives that crossed team boundaries, and of influencing without direct authority
Strong product and platform judgment: you weigh business outcomes, developer experience, and technical trade-offs
Clear written and spoken communication suited to a remote, async-first company
Track record of mentoring other experienced engineers
Ownership mentality with a bias for action and iterative delivery in ambiguous, fast-moving environments
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 evolving an internal platform or tool into a widely adopted, production-grade service
Knowledge of security, compliance, 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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Build context on Docker's current AI and developer tooling landscape, including existing AI experiments, LLM provider relationships, and architecture decisions to date
Meet the Senior Manager, the team's Principal engineers, engineering leadership, and key stakeholders in product engineering to understand needs, pain points, and constraints
Review existing AI tool prototypes and internal experiments to learn what is working and what is not
Get hands-on with the team's services and ship a useful production change
Shadow on-call and learn the operational profile of the team's services
Work with the Senior Manager to agree on initial technical priorities
Lead the technical framing and delivery plan for one significant problem, from requirements through to production
Document the architecture, technology choices, and integration patterns for that work, including ADRs
Ship a production AI developer tool that sets patterns the team can reuse (for example an AI-powered code review agent, a deployment diagnostic agent, or an on-call assistant)
Contribute to the technical foundations of the self-service platform: deployment pipeline, observability integration, security controls, and cost management
Lead design reviews for your area of the roadmap and begin mentoring other engineers
Take part in the on-call rotation
Be the trusted technical lead for your area of AI Developer Tools, and a sought-after partner for teams across Docker
Deliver at least one material outcome that spans multiple teams or systems
Have multiple production AI tools demonstrating value across Docker's engineering organization
Help build a self-service platform that lets multiple teams build, deploy, and operate their own AI tools with little friction
Define and instrument how the team measures AI tool adoption, effectiveness, and developer productivity impact
Grow other senior engineers into broader ownership, and reduce the number of decisions that depend on a small set of people
Visa sponsorship: Docker considers visa sponsorship on a case-by-case basis based on business needs.
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All full-time roles include equity. Final compensation is determined on factors including but not limited to experience, skills, qualifications, and location.
United States: $170,350 – $275,550 base
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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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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.
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