Senior Manager of Engineering, Docker
AI Developer Tools
$226.6-318.5k
Offers equity
AWS
Kubernetes
GCP
Terraform
Azure
Grafana
Pulumi
DevOps
Senior and Expert level
Remote from US
More information about location
App development platform
Be an early applicant
App development platform
501-1000 employees
B2BInternal toolsSaaSDevOpsCloud Computing
Be an early applicant
$226.6-318.5k
Offers equity
AWS
Kubernetes
GCP
Terraform
Azure
Grafana
Pulumi
DevOps
Senior and Expert level
Remote from US
More information about location
501-1000 employees
B2BInternal toolsSaaSDevOpsCloud Computing
Company mission
To increase the time developers spend on innovation, and decrease the time they spend on everything else.
Role
Who you are
- 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
- 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
What the job involves
- 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
- 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
- First 30 Days:
- 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
- First 90 Days:
- 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
- First Year Outlook:
- 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
Our take
Docker supplies a hub and desktop solution to simplify the workflow of app development teams. Originally known for popularising the idea of containerising software, it saw itself outpaced by Kubernetes. However, the company has identified a problem with the growing complexity of containerisation, with some apps consisting of dozens or even hundreds of containers - which is what it now addresses.
Docker has had a difficult time in the recent past, with 2019 seeing it sell off its enterprise business, reduce its workforce by hundreds, and change its leadership team. As a well known brand and ecosystem for containerisation, however, it aims to lure developers back to its product as well as to take advantage of the growing global demand for app development.
Returning to focus on developers rather than large companies was certainly a gamble, but one that seems to have paid off. The company has returned to profitability and raised Series C funding in 2022, which is cited to fund hiring new talent, ramping up its business, and continuing to develop and refine its product. It's also embarked on a series of acquisitions to bolster its business, with speculation mounting about its possible sale in the near future.
Steph
Company Specialist at Welcome to the Jungle
Insights
Top investors
47% employee growth in 12 months
Company
Funding (last 2 of 11 rounds)
Mar 2022
$105m
SERIES C
Mar 2021
$23m
SERIES B
Total funding: $498m
Company benefits
- 100% company paid medical premiums for employees and dependents
- Flexible Time Off Policy
- “Whaleness” Days — At least 1 company wide day off per month
- Employer Paid Holidays
- Generous Maternity and Parental Leave
- Home Office Set Up Budget
- Monthly Technology Stipend
- Training Allowances
- Life and Disability Insurance
- Retirement Plans
- Virtual and In-Person Social Events
- Docker Swag
- Quarterly Hackathons
- Virtual Coffee with Co-Workers
Company HQ
China Basin, San Francisco, CA
Leadership
Don Johnson
(CEO)Previously Executive Vice President of Infrastructure at Oracle, was a Principal Engineer at Amazon and a Lead Software Engineer at RealNetworks.
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