Location requirements
At Docker, we make app development easier so developers can focus on what matters. Our remote-first team spans the globe, united by a passion for innovation and great developer experiences. With over 20 million monthly users and 20 billion image pulls, Docker is the #1 tool for building, sharing, and running apps—trusted by startups and Fortune 100s alike. We’re growing fast and just getting started. Come join us for a whale of a ride!
Docker seeks a Senior Software Engineer to join our new AI Developer Tools team at the forefront of AI-powered developer productivity. You'll build cutting-edge AI agents and tools that transform how developers write code, debug issues, deploy applications, and respond to incidents—both internally at Docker and for our customers worldwide.
This is an opportunity to work at the intersection of AI and developer experience, building production systems that leverage LLMs and AI agents to accelerate developer workflows. You'll architect and implement AI-powered tools such as code review assistants, automated test generators, deployment diagnostics agents, and on-call assistance tools. You'll also contribute to the self-service platform that enables teams across Docker to rapidly build and deploy their own AI developer tools.
Your work will directly impact how Docker's engineers build and operate services powering 20 million users, and as these tools mature, you'll help transform them into commercial offerings for Docker's customers.
You'll collaborate closely with the Principal Engineer on technical architecture, partner with product and design teams on user experience, and work autonomously in a fast-paced, remote-first environment where rapid iteration and shipping are core values.
You're passionate about developer experience and excited by the potential of AI to transform how developers work. You have hands-on experience building production systems with LLMs or AI agents, and you understand the nuances of prompt engineering, agent orchestration, and evaluating AI system effectiveness. You think in terms of products and platforms, balancing technical excellence with pragmatism to ship iteratively while maintaining high quality bars. You're comfortable navigating the rapidly evolving AI/LLM landscape, experimenting with new tools and approaches, and making pragmatic technology choices. You thrive in ambiguity, enjoy rapid prototyping, and have strong opinions on what makes developer tools that engineers love to use. Most importantly, you're collaborative, communicate clearly in remote environments, and take ownership of your work from design through deployment and operations.
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
5+ 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
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
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 such as deployment systems, observability platforms, and CI/CD pipelines
Explore Docker's LLM provider relationships, AI technology choices, and existing integration patterns
Make your first contributions to the AI Developer Tools codebase through bug fixes, small features, or documentation improvements
Participate in design discussions and code reviews to understand team technical standards and decision-making processes
Take ownership of and deliver your first significant AI developer tool feature (e.g., code review assistant capability, test generation module, or deployment diagnostic agent component)
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
Begin mentoring other engineers through code reviews and technical discussions
Establish monitoring and instrumentation for AI tools you've shipped to measure adoption and effectiveness
Support hiring efforts by participating in interviews and providing feedback on candidates
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 across Docker's engineering organization and measurable productivity improvements
Contribute to technical strategy and architectural decisions for AI developer tools alongside Principal Engineer
Mentor engineers on AI/LLM integration patterns and developer tool best practices
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
Establish yourself as a go-to expert for AI in developer workflows within Docker's engineering organization
We use Covey as part of our hiring and / or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 13, 2024.
Please see the independent bias audit report covering our use of Covey here.
Perks
Freedom & flexibility; fit your work around your life
Designated quarterly Whaleness Days plus end of year Whaleness break
Home office setup; we want you comfortable while you work
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
Docker Swag
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.
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Docker is the world's leading software container platform. Developers use Docker to eliminate "works on my machine" problems when collaborating on code with co-workers. Operators use Docker to run and manage apps side-by-side in isolated containers to get better compute density. Enterprises use Docker to build agile software delivery pipelines to ship new features faster, more securely and with confidence for both Linux and Windows Server apps.
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