Technical Leadership and Architecture

• 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

Systems Design and Implementation

• 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

Strategic Impact and Innovation

• 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

Leadership and Mentorship

• 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.

Required

• 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

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 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)

What to Expect

First 30 Days

• 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

First 90 Days

• 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

First Year Outlook

• 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

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

Compensation & Equity

United States: $170,350 – $275,550 + 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