Software Engineer III, AI Developer Tools
RemoteMidNADiagnosticsArtificial IntelligenceCloud ComputingDeveloper ToolsSoftware EngineerAI EngineerDockerRustJavaGoPython
Upload My Resume
Drop here or click to browse · PDF, DOCX, TXT
Requirements
- 3-5 years building production-grade backend systems or developer-facing tools with strong software engineering fundamentals
- Hands-on production experience with AI/ML technologies including practical experience with LLM APIs (OpenAI, Anthropic, etc.), prompt engineering, and 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
- Demonstrated ability to work independently on day-to-day work with general guidance on new projects
- 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
- Ability to build effective working relationships across multiple teams
- Ownership mentality with bias for action and iterative delivery
- Comfortable working autonomously across distributed teams and navigating ambiguity
- Preferred:
- 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
- Understanding of software design patterns and distributed systems principles
- What to Expect
- What to Expect
- First 30 Days
- Get up to speed on Docker's AI Developer Tools vision, current Agent Dev project status, and existing AI tool prototypes
- Meet your team, Principal Engineer, Senior Manager, and key stakeholders across product engineering and platform teams
- Understand Docker's developer tooling landscape including deployment systems, observability platforms, and CI/CD pipelines
- Explore Docker's LLM provider relationships, AI technology choices, and existing integration patterns
- Make meaningful contributions to the AI Developer Tools codebase through features or improvements
- Participate in design discussions and code reviews to understand team technical standards and decision-making processes
- Begin building relationships with engineers across multiple teams
- First 90 Days
- Take ownership of and deliver significant features with measurable impact (e.g., complete AI agent capability, LLM integration improvement, or platform infrastructure component)
- Work with increasing independence on day-to-day tasks; demonstrate good judgment on when to ask for guidance
- Contribute to platform infrastructure improvements that enable faster development and deployment of AI tools
- Collaborate with product and design teams on feature requirements and user experience for AI developer tools
- Participate in user research and customer calls to understand developer pain points and validate AI tool effectiveness
- Help other engineers through code reviews and technical discussions
- Establish monitoring and instrumentation for AI tools you've shipped to measure adoption and effectiveness
- First Year Outlook
- First Year Outlook
- Own significant components of AI developer tools platform with responsibility for design, implementation, and operations
- Ship multiple production AI agents and tools with demonstrated adoption and measurable productivity improvements
- Work largely independently on routine work; exercise good judgment within defined processes
- Build strong working relationships across Docker with product, platform, and engineering teams
- Act as a reliable technical resource for teammates
- Demonstrate emerging strategic thinking in your approach to problems and solutions
- Drive measurable improvements in developer productivity metrics such as AI tool adoption, commit frequency, PR velocity, deployment times, and CI run times
- Participate in productization efforts as internal AI tools evolve into customer-facing offerings
- Continue growing your expertise in AI/ML technologies and platform engineering
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 agents that simplify on-call tasks when handling incidents
- 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 and Senior Engineers on architecture, work with product and design teams on features and UX, and collaborate with platform teams (Infrastructure, Security, Data) on integrations; build effective partnerships across multiple teams
- Act as Technical Resource: Help teammates solve problems and share knowledge through code reviews and technical discussions
- 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
- Document and Share: Create clear technical documentation for features you build; share patterns and learnings with the team
- 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
Benefits
- Freedom & flexibility; fit your work around your life
- Designated quarterly Whaleness Days plus end of year Whaleness break
- 16 weeks of paid Parental leave
- Technology stipend equivalent to $100 net/month
- PTO plan that encourages you to take time to do the things you enjoy
- Training stipend for conferences, courses and classes
- Equity; we are a growing start-up and want all employees to have a share in the success of the company
- Medical benefits, retirement and holidays vary by country
- Remote-first culture, with offices in Seattle and Paris
- Docker embraces diversity and equal opportunity. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better our company will be.
- Due to the remote nature of this role, we are unable to provide visa sponsorship.
Similar Jobs
22h ago
Yesterday