Docker
Staff Software Engineer, AI Developer Tools
$170K–$276K
San Francisco, CA +1 · Remote · Staff · Full-time · Posted Oct 14, 2026
- Location
- San Francisco, CA
Seattle, WA - Experience
- 8+ years of software engineering experience, with demonstrated technical leadership on production systems at scale.
- Education
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- Visa sponsorship
- Docker considers visa sponsorship on a case-by-case basis based on business needs.
About the job
Sets technical direction and builds production AI developer tools and a self-service platform that enables Docker teams to develop, deploy, and operate their own AI tools.
What you'll do
- Design, build, and operate AI agents for developer productivity, including code review, testing, deployment diagnostics, and incident response.
- Create scalable platform infrastructure, deployment patterns, observability, security controls, and operational standards for AI tools.
- Lead cross-team architecture and delivery, evaluate AI technologies, and mentor engineers.
What they ask for
- Hands-on production experience building LLM-powered applications, AI agents, or AI-assisted developer tools.
- Experience designing scalable distributed systems and developer-facing platforms, with cloud-native infrastructure including Kubernetes, GitOps, observability, and CI/CD.
- Proficiency in Go, Rust, Java, or Python; experience leading complex cross-team initiatives and mentoring experienced engineers.
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Nice to have
- Experience with MCP (Model Context Protocol) or similar AI agent integration standards.
- Background in developer productivity, DevOps, SRE, or platform engineering.
Pay
USD $170,350 – $275,550 base salary
Tools
LLM APIs (OpenAI, Anthropic, and others), Kubernetes, GitOps, CI/CD, Go, Rust, Java, Python
AI focus
Builds production AI agents and developer tools, including code review and refactoring assistants, test generators, deployment diagnostics, and incident-response automation; also builds a self-service platform for teams to prototype and deploy AI tools.
Model-written summary · the original posting is authoritative