Senior Software Engineer, AI Developer Tools

Docker•San Francisco, CA

2d•$160,900 - $260,700•Remote

About The Position

Docker is looking for a Senior Software Engineer to build AI-powered developer tools and the platform behind them on our AI Developer Tools team. You will work at the intersection of AI, developer experience, and platform engineering, building tools that make Docker's engineers more productive and the platform that lets teams across Docker prototype, deploy, and scale their own AI developer tools. As a Senior Engineer you will design, build, and operate: AI-Powered Developer Tools. 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. Platform infrastructure that lets product and platform teams across Docker unblock themselves by prototyping, building, and deploying their own AI developer tools, using the patterns and standards the team sets. You will report to the Senior Manager, AI Developer Tools, and work closely with the team's Principal and Staff engineers, product engineering teams, and platform teams across Docker.

Requirements

6+ years of software engineering experience building and operating production systems
Hands-on experience building LLM-powered applications, AI agents, or AI-assisted developer tools
Working knowledge of LLM APIs (OpenAI, Anthropic, and others), prompt engineering, agent orchestration frameworks, and practical uses of AI in software development workflows
Experience building and operating scalable backend services, distributed systems, or developer-facing platforms
Experience with cloud-native infrastructure, including Kubernetes, CI/CD pipelines, and observability systems
Proficiency in Go (preferred), Rust, Java, or Python, with strong software engineering fundamentals
Experience building developer tools, platform systems, or internal tools that other engineers rely on
Good product judgment: you weigh developer experience and technical trade-offs, and you ask clarifying questions before committing to a design
Clear written and spoken communication suited to a remote, async-first company
Ownership mentality with a bias for action and iterative delivery in ambiguous, fast-moving environments
Comfortable working autonomously in a distributed, remote-first team
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 with GitOps deployment patterns (ArgoCD) or Grafana-based observability
Familiarity with security and operational practices for production AI systems
Experience with infrastructure-as-code (Terraform, Pulumi) and major cloud platforms (AWS, GCP, Azure)

Responsibilities

Building and Operating AI Developer Tools
Design, build, and operate production AI agents for developer productivity, including code review assistants, test generators, deployment diagnostics, and incident response automation
Integrate LLM providers, agent orchestration frameworks, and MCP servers into reliable tools, following the team's patterns and standards
Build systems that help Docker's engineers adopt AI-native development tools
Build integrations between AI developer tools and existing developer infrastructure such as CI/CD pipelines, observability platforms, and deployment systems
Instrument your tools so the team can measure adoption, effectiveness, and developer satisfaction
Self-Service Platform
Build and improve platform capabilities, including deployment pipelines (ArgoCD/GitOps), observability integration (Grafana), security controls, and cost management, that help teams ship their own AI tools faster
Help reduce time-to-production for new products from weeks to days
Contribute to reliability, security, and performance standards for AI systems, including SLOs, monitoring, and incident response
Design and Delivery
Take features and services from requirements to production. Ask about scale, users, and constraints before designing, and write down your assumptions
Write clear design documents for the work you own, and participate in design reviews for the team
Review code thoroughly and share what you learn with the rest of the team
Take part in the team's on-call rotation, and help improve alerts, runbooks, and tooling so the next incident is easier
Follow the AI-assisted engineering practices the team sets: use AI tools critically, validate their output, and stay accountable for what you ship
Collaboration and Growth
Work with the Senior Manager and the team's Principal and Staff engineers to understand priorities and shape your work
Partner with Infrastructure, Security, and Data teams on integrations and shared standards
Help newer engineers get up to speed on the team's tools, patterns, and practices
Keep up with emerging AI/LLM technologies and agent frameworks, and bring useful findings back to the team

Benefits

Generous PTO
designated quarterly Whaleness Days
a designated end-of-year Whaleness break
Home office support
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
16 weeks of paid parental leave after six months of employment
Equity for all full-time employees
Medical
retirement
paid holidays vary by country