Job Title: AI Developer Tools Company Name: Docker Job Url: https://simplify.jobs/jobs?query=Ai+engineer&state=North+America&points=83%3B-170%3B7%3B-52&experience=Mid+Level%3BSenior&category=AI%2FML%2FGenAI+Engineering%3BBackend+Engineering%3BFrontend+Engineering%3BFull-Stack+Engineering%3BSoftware+Engineering&education=Bachelor%27s&mostRecent=true&excludeApplied=true&jobId=f82e459c-9a7b-4718-9bee-9cc6b9a595a2&jobType=Full-Time%3BPart-Time%3BContract&workArrangement=Remote Job Description: AI Developer Tools Confirmed live in the last 24 hours Unlock job analytics with Simplify+ Docker 501-1,000 employees Containerization platform for building apps Compensation Overview $184.6k - $260.7k/yr + Equity Senior, Expert H1B Sponsorship Available Seattle, WA, USA Remote Remote-first culture; offices in Seattle and Paris. Category Software Engineering (2) AI/ML/GenAI Engineering , Backend Engineering Required Skills Skills that you prefer have been highlighted Kubernetes Microsoft Azure Python Java Docker Microservices AWS REST APIs Google Cloud Platform LLM Rust OpenAI Go Observability Get referrals → You have ways to get a Docker referral from your network. Applications through a referral are 3x more likely to get an interview! History Summary Full Job Posting You match the following Docker's candidate preferences Employers are more likely to interview you if you match these preferences: Degree Experience You have ways to get a Docker referral from your network. Get referrals → Requirements 6+ years building production-grade backend systems or developer-facing tools Hands-on experience with AI/ML technologies such as practical production experience with LLM APIs (OpenAI, Anthropic, etc.), prompt engineering, or AI agent development Proficiency in Go (preferred), Rust, Java, or Python with strong software engineering fundamentals Experience designing and building distributed systems, microservices, or platform infrastructure Strong understanding of cloud-native systems (AWS, GCP, or Azure), APIs, and data stores Solid grasp of CI/CD, automated testing, code review practices, and modern development workflows Product-minded approach to building developer tools with focus on user experience and measurable outcomes Excellent communication skills in remote, asynchronous environments with ability to document technical decisions clearly Ownership mentality with bias for action and iterative delivery Comfortable working autonomously across distributed teams and navigating ambiguity Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience Responsibilities Build AI-Powered Developer Tools: Design, implement, and ship production-ready AI agents and tools that accelerate developer productivity such as code review and refactoring assistants, automated test generators, local environment setup tools, deployment pipeline diagnostic agents, and on-call assistance tools Implement LLM Integrations: Build robust, production-grade integrations with LLM APIs (OpenAI, Anthropic, etc.) such as prompt engineering, response parsing, error handling, rate limiting, cost management, and performance optimization Develop Agent Orchestration Systems: Create agent frameworks and orchestration systems that enable complex multi-step workflows, tool calling, context management, and agent-to-agent communication Contribute to Platform Infrastructure: Build self-service platform capabilities that enable teams across Docker to rapidly deploy and operate their own AI developer tools such as deployment pipelines, observability integration, security controls, and operational tooling Drive Adoption of AI-Native Development: Build tools and programs that accelerate adoption of AI developer tools such as Claude Code, Cursor, and Warp across Docker's engineering organization Ensure Production Quality: Write well-tested code with strong test coverage (unit, integration, end-to-end); establish monitoring, alerting, and operational excellence for AI systems Collaborate Cross-Functionally: Partner with Principal Engineer on architecture, work with product and design teams on features and UX, and collaborate with platform teams (Infrastructure, Security, Data) on integrations Participate in Operations: Take part in on-call rotation for AI developer tools; respond to incidents, debug production issues, and drive continuous improvement of system reliability Mentor and Share Knowledge: Guide other engineers through code reviews, pair programming, and technical discussions; document patterns and best practices for AI tool development Measure and Iterate: Instrument AI tools to measure adoption, effectiveness, and developer productivity impact; iterate based on data and user feedback to continuously improve developer experience On-Call Rotation: Take part in on-call rotation for your team; respond to incidents, debug production issues, and drive continuous improvement of system reliability Desired Qualifications Experience with AI agent frameworks (LangChain, LangGraph, CrewAI, or similar) Contributions to open source AI tools, developer tooling, or platform engineering projects Experience with MCP (Model Context Protocol) or similar AI agent integration standards Background in developer productivity, DevOps, SRE, or platform engineering domains Experience with Kubernetes, Docker, and container orchestration Knowledge of developer tools ecosystems (IDEs, CI/CD platforms, observability tools) Experience with infrastructure-as-code (Terraform, Pulumi) and GitOps deployment patterns (ArgoCD, FluxCD) Understanding of security, compliance, and operational best practices for production AI systems Keyword Match Needs Work - 13 out of 27 found Docker Website View Company Profile Docker builds, shares, and runs applications in isolated containers by packaging an application and its dependencies into a container image that runs consistently across different systems. It provides tools like Docker Desktop for local development, Docker Hub as a container image repository, and a command-line interface to build, run, and manage containers. It differentiates itself with a large ecosystem, an official image repository, and integrated tools that support an end-to-end container workflow. The company aims to help developers consistently build, share, and run software across any environment, using a freemium model with subscription tiers and additional services. Company Size 501-1,000 Company Stage Series C Total Funding $498M Headquarters Palo Alto, California Founded 2013