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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 Manager of Engineering to build and lead a new AI Developer Tools team that will revolutionize how developers build software—both internally at Docker and for our customers worldwide. This is a rare opportunity to establish a greenfield team at the intersection of AI and developer experience, where you'll build cutting-edge AI-powered developer tools and create the platform that enables teams across Docker to rapidly prototype, deploy, and scale their own AI developer tools.
Build AI-Powered Developer Tools: Create and ship innovative AI agents and tools that accelerate developer productivity, provide observability insights, and automate operational reviews. Build tools and programs that make it easier for teams to adopt an AI-native mindset and accelerate adoption and usage of AI developer tools such as Claude Code, Cursor, Warp, or other AI tools as they gain traction within Docker teams. Your team will build tools such as 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 tasks when handling incidents. These tools will directly impact how Docker's engineers build, deploy, and operate services powering 20 million users.
Enable Self-Service AI Developer Tools Platform: Build the foundational infrastructure and self-service capabilities that empower product and platform teams across Docker to unblock themselves by rapidly prototyping, building, and deploying their own AI developer tools. Teams will be able to experiment with AI solutions for their unique pain points without waiting for centralized platform teams, iterate quickly on tool ideas, and graduate successful prototypes into production-ready services. The result: development teams ship capabilities to customers faster, operations teams spend less time firefighting, and engineering velocity compounds across the organization.
As these internal AI developer tools demonstrate value and gain traction, you'll partner with product leadership to explore productization opportunities—transforming proven internal tools into new commercial offerings for Docker's customers.
Reporting to the Director of Platform Infrastructure & AI Developer Tools, you'll work closely with engineering leadership across Docker, product engineering teams, platform teams, and ultimately customers as internal tools evolve into product offerings.
You're energized by the intersection of AI, developer experience, and platform engineering. You've built teams before and have a track record of shipping developer-facing tools or platforms that engineers love to use. You think in terms of platforms and leverage—building once and enabling dozens of teams to move faster. You understand AI technology deeply enough to separate signal from noise, making pragmatic decisions about when to build custom solutions versus integrating existing tools. You have a product mindset for internal tooling and can envision how today's internal experiments become tomorrow's commercial offerings. You thrive in ambiguity, building roadmaps from first principles while staying grounded in real developer pain points. Most importantly, you know how to attract exceptional engineers, build team culture from scratch, and create an environment where innovation thrives.
Build and Scale the AI Developer Tools Team: Hire, onboard, and develop a high-performing engineering team from the ground up with expertise in AI/LLM integration, platform engineering, and developer experience; establish team culture, technical standards, and operating norms
Ship AI-Powered Developer Tools: Deliver production-ready AI agents for developer productivity (automated PR reviews, code generation, documentation), observability insights (anomaly detection, root cause analysis), and operational automation (deployment pre-reviews and failure investigations, incident response assistance)
Build Self-Service AI Developer Tools Platform: Create and operate the foundational infrastructure that enables teams across Docker to build, deploy, host, and scale their own AI developer tools including deployment frameworks (ArgoCD/GitOps), observability integration (Grafana), security controls, cost management, and operational tooling
Drive Platform Adoption and Developer Experience: Establish the AI Developer Tools platform as the default path for teams building AI-powered tooling; deliver self-service capabilities, comprehensive documentation, templates, and best practices that reduce time-to-production from weeks to days
Partner on AI Strategy and Technology: Work closely with engineering leadership and Agent Dev technical leadership to align on AI technology choices, architectural patterns, and integration strategies; stay current on LLM advancements and developer tooling trends
Explore Productization Opportunities: As internal AI developer tools demonstrate value, partner with product management and go-to-market teams to evaluate commercial viability; prototype, validate, and transition successful internal tools into customer-facing product offerings
Deliver Measurable Impact: Define and track success metrics for both AI developer tools (adoption rates, productivity gains, time saved) and platform capabilities (time-to-deploy new tools, number of teams using platform, operational efficiency)
Cross-Functional Collaboration: Partner with product engineering teams (Hub, Registry, Cloud/AI, Scout, Accounts & Billing), platform teams (Infrastructure, Security, Data), and product leadership to understand requirements, gather feedback, and align on priorities
Operational Excellence: Ensure reliability, security, and performance of AI developer tools and the hosting platform; establish SLOs, monitoring, and incident response for production AI systems serving internal developers
Team Development and Culture: Mentor and grow engineers on your team; foster a culture of experimentation, rapid prototyping, and learning; attract diverse talent excited about AI and developer experience
5+ years managing high-performing engineering teams, with demonstrated experience hiring, developing, and retaining diverse technical talent; experience building teams from scratch highly valued
5+ years as a software developer with hands-on experience building developer tools, platform engineering systems, DevOps, or SRE infrastructure
Strong understanding of AI/ML technologies, LLM integration patterns, and practical applications of AI in developer workflows; hands-on experience building AI-powered tools or agents preferred
Track record of building platforms or internal tools that enable other teams and measurably improve developer productivity
Deep technical knowledge of modern cloud-native infrastructure including Kubernetes, GitOps deployment patterns, observability systems, and CI/CD pipelines
Experience with infrastructure-as-code frameworks (Terraform, Pulumi) and cloud platforms (AWS, GCP, Azure)
Product mindset with ability to envision how internal tools can become commercial offerings; experience with productization of internal platforms a plus
Strong verbal and written communication skills with ability to influence cross-functional stakeholders, evangelize platform adoption, and partner with product and go-to-market teams
Comfortable with autonomous work, ambiguity, and building in uncharted territory; proven ability to define roadmaps and priorities from first principles
Passion for developer experience, AI innovation, and creating leverage through platform thinking
Understand Docker's current AI and developer tooling landscape including Agent Dev project status, existing internal AI experiments, and developer pain points ripe for AI solutions
Meet with engineering leadership across Docker, Agent Dev technical leadership, and key stakeholders across product engineering to understand AI strategy, technology choices, and priority use cases
Identify priority AI developer tool initiatives including productivity agents and observability insights
Assess current developer tooling infrastructure (deployment, observability, security) to understand what can be leveraged and what needs to be built for AI tools hosting platform
Begin hiring process for initial team members with focus on engineers experienced in AI/LLM integration, platform engineering, and developer experience
Hire and onboard initial team members with clear specialization areas (AI tools development, platform infrastructure, developer experience)
Ship first production AI developer tool demonstrating value (e.g., automated PR review agent, deployment failure investigation assistant, or observability insights bot) with measurable adoption and impact metrics
Deliver v1 of self-service AI Developer Tools platform enabling at least one other team to deploy their own AI agent to production with reduced friction
Establish team goals, success metrics, and OKRs including AI tool adoption rates, platform usage, developer productivity improvements, and operational metrics
Create comprehensive documentation, templates, and best practices for teams building AI developer tools on the platform
Present 90-day retrospective including shipped tools, platform capabilities, adoption metrics, team composition, and next-phase roadmap to leadership
Build a fully-staffed, high-performing AI Developer Tools team with clear expertise distribution across AI/LLM engineering, platform infrastructure, and developer experience
Ship multiple production AI developer tools with demonstrated adoption across Docker's engineering organization and quantified productivity improvements (time saved, faster deployments, reduced incidents, faster failure resolution)
Establish mature, self-service AI Developer Tools platform with multiple teams successfully building and hosting their own AI agents
Deliver measurable improvements in AI tool adoption and usage, early-stage development metrics including commit frequency and PR velocity, deployment times, CI run times, and incident response times, while ensuring pipeline rollback rates remain stable or improve
Position AI Developer Tools team as center of excellence for AI in developer workflows at Docker, with regular demos, evangelism, and knowledge sharing across engineering
Demonstrate sustained positive impact on direct reports through career development, skill growth in AI technologies, and team satisfaction scores
Define multi-year strategic roadmap for AI Developer Tools including advanced agents, expanded platform capabilities, and additional productization opportunities
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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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