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Salary: USD 170,350 - 275,550 / annual
Docker is seeking a Staff Software Engineer to set technical direction for the AI Developer Tools team. You will work at the intersection of AI, developer experience, and platform engineering, owning technical design and delivery across two key areas:
**AI-Powered Developer Tools**: Design and build AI agents and tools that accelerate developer productivity, including AI-powered code review and refactoring assistants, automated test generators, local environment setup tools, deployment pipeline diagnostic agents, and on-call incident assistance. Help teams adopt AI-native workflows and accelerate adoption of tools like Claude Code and Codex.
**Self-Service AI Developer Tools Platform**: Design and build platform infrastructure enabling product and platform teams across Docker to prototype, build, and deploy their own AI developer tools. Establish patterns and standards that allow teams to experiment, iterate quickly, and graduate prototypes into production services.
You will report to the Senior Manager of AI Developer Tools and work closely with Principal engineers, engineering leadership, product engineering teams, and platform teams.
**Key Responsibilities**:
- Set technical direction and own architecture for AI-powered developer tools and self-service platform
- Turn ambiguous problems into clear requirements, plans, and staged decisions
- Establish patterns, standards, and best practices for LLM integration, agent development, and production AI systems
- Shape platform capabilities including deployment frameworks (ArgoCD/GitOps), observability (Grafana), security controls, and operational tooling
- Design reliable, scalable infrastructure for hosting AI agents and developer tools
- Design, build, and operate production AI agents for developer productivity
- Build self-service platform capabilities reducing time-to-production for new AI tools
- Set and meet reliability, security, and performance standards including SLOs, monitoring, and cost management
- Evaluate emerging AI/LLM technologies and recommend adoption strategies
- Mentor senior engineers on AI/LLM integration patterns and platform engineering
- Lead design reviews and create reusable patterns and architectural decision records
- Participate in on-call rotation and improve alerts, runbooks, and tooling based on learnings
**First 30 Days**: Build context on Docker's AI landscape, meet stakeholders, review prototypes, ship a production change, shadow on-call, and align on technical priorities.
**First 90 Days**: Lead technical framing and delivery for a significant problem, document architecture and technology choices, ship a production AI developer tool, contribute to self-service platform foundations, lead design reviews, and participate in on-call rotation.
**First Year**: Be the trusted technical lead for AI Developer Tools, deliver material outcomes spanning multiple teams, have multiple production AI tools demonstrating value, help build a self-service platform, define how the team measures AI tool adoption and effectiveness, and grow other senior engineers.
**Requirements**:
- 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, others), prompt engineering, agent orchestration frameworks, and practical AI uses 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 design
- Track record of leading complex cross-team initiatives and influencing without direct authority
- Strong product and platform judgment weighing business outcomes, developer experience, and technical trade-offs
- Clear written and spoken communication suited to remote, async-first company
- Track record of mentoring other experienced engineers
- Ownership mentality with bias for action and iterative delivery in ambiguous, fast-moving environments
- Bachelor's degree in Computer Science, Engineering, or 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 internal platform or tool into 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)
