Staff Software Engineer, AI Developer Tools

Docker • San Francisco, CA

2d • $170,350 - $275,550 • Remote

About The Position

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.

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, 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

Nice To Haves

• 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)

Responsibilities

• 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

Benefits

• Generous PTO
• designated quarterly Whaleness Days
• designated end-of-year Whaleness break
• Home office support
• Technology stipend – Equivalent to US$100 net per month
• Learning & development – Annual stipend for conferences, courses, certifications, and continued learning
• 16 weeks of paid parental leave after six months of employment
• Equity for all full-time employees
• Comprehensive benefits – Medical, retirement, and paid holidays vary by country.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior