Docker
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 Principal Engineer
to define the technical vision and architecture for our new AI Developer Tools team. This is a rare opportunity to establish the technical foundation for how AI will transform developer productivity—both internally at Docker and for our customers worldwide. You'll work at the intersection of AI, developer experience, and platform engineering, architecting cutting‑edge AI‑powered developer tools and building the platform that enables teams across Docker to rapidly prototype, deploy, and scale their own AI developer tools.
As Principal Engineer, you’ll own the technical strategy for two critical areas:
AI‑Powered Developer Tools Architecture:
Define the technical architecture for innovative AI agents and tools that accelerate developer productivity, provide observability insights, and automate operational reviews. Lead the design and implementation of tools that make it easier for teams to adopt an AI‑native mindset and accelerate adoption of AI developer tools such as Claude Code, Cursor, Warp, or other AI tools as they gain traction within Docker teams. You’ll architect 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.
Self‑Service AI Developer Tools Platform:
Design and build the foundational platform infrastructure that empowers product and platform teams across Docker to unblock themselves by rapidly prototyping, building, and deploying their own AI developer tools. Establish technical standards, architectural patterns, and best practices that enable teams to experiment with AI solutions for their unique pain points, iterate quickly on tool ideas, and graduate successful prototypes into production‑ready services.
As internal AI developer tools demonstrate value and gain traction, you’ll partner with product and engineering leadership to evaluate productization opportunities—defining the technical architecture for transforming proven internal tools into new commercial offerings for Docker’s customers.
Reporting to the Director of Platform Infrastructure & AI Developer Tools, you’ll collaborate closely with engineering leadership across Docker, the AI Developer Tools Senior Manager, product engineering teams, platform teams, and ultimately customers as internal tools evolve into product offerings.
What Would Make Someone Successful in This Role You’re a technical leader who excels at the intersection of AI, developer experience, and platform engineering. You have deep expertise in LLM integration, AI agents, and practical applications of AI in developer workflows, with a track record of shipping production AI systems at scale. You think in systems and platforms, designing architectures that enable leverage—building once and enabling dozens of teams to move faster. You understand the nuances of developer tooling and can articulate clear opinions on what makes tools that developers love to use. You have exceptional judgment on when to build custom solutions versus integrate existing tools, and you’re comfortable navigating the rapidly evolving AI/LLM landscape. You balance technical excellence with pragmatism, shipping iteratively while maintaining high quality bars. Most importantly, you lead through influence and mentorship, elevating the entire engineering organization's technical capabilities around AI and developer tooling.
Responsibilities Technical Leadership & Architecture
Define the long‑term technical vision and architecture for AI‑powered developer tools and the self‑service platform that enables teams to build their own AI agents
Establish architectural patterns, technical standards, and best practices for LLM integration, AI agent development, and production AI systems serving developers
Lead technical strategy for platform capabilities including deployment frameworks (ArgoCD/GitOps), observability integration (Grafana), security controls, and operational tooling for AI developer tools
Design highly available, scalable infrastructure for hosting AI agents and developer tools with predictable performance and intelligent resource management
Drive technical decisions on AI technology choices, LLM provider strategies, prompt engineering approaches, and agent orchestration frameworks
Partner with Senior Manager and product leadership to align technical architecture with business objectives and productization opportunities
Systems Design & Implementation
Architect and build production‑ready AI agents for developer productivity including code review assistants, test generators, deployment diagnostics, and incident response automation
Design and implement the self‑service platform infrastructure that reduces time‑to‑production for new AI tools from weeks to days
Build systems that accelerate adoption of AI‑native development tools (Claude Code, Cursor, Warp) across Docker’s engineering organization
Establish reliability, security, and performance standards for AI systems including SLOs, monitoring, incident response, and cost management
Design integration points between AI developer tools and existing developer infrastructure (CI/CD pipelines, observability platforms, deployment systems)
Lead technical implementation of AI tools that improve early‑stage development metrics (commits, PRs), deployment pipelines, and incident response while maintaining pipeline stability
Strategic Impact & Innovation
Evaluate emerging AI/LLM technologies, developer tooling trends, and agent frameworks to inform Docker’s technical strategy
Define technical approach for productizing internal AI developer tools into customer‑facing offerings
Drive technical standards for measuring AI tool effectiveness including adoption metrics, productivity gains, and developer satisfaction
Optimize AI tool performance, cost efficiency, and developer experience through architectural improvements and LLM provider strategies
Lead cross‑functional technical discussions influencing company‑wide AI and developer tooling architecture
Leadership & Mentorship
Mentor senior and staff engineers on AI/LLM integration patterns, agent development, and platform engineering best practices
Lead design reviews and technical decision‑making for production AI systems
Foster culture of technical excellence, experimentation, and rapid prototyping within AI Developer Tools team
Serve as primary technical contact and thought leader for AI in developer workflows across Docker’s engineering organization
Collaborate with platform teams (Infrastructure, Security, Data) to establish shared technical standards and integration patterns
Qualifications Required:
10+ years software engineering experience with 3+ years in Staff or Principal Engineer roles
Deep expertise in AI/ML technologies with hands‑on production experience building LLM‑powered applications, AI agents, or AI‑assisted developer tools
Strong understanding of LLM APIs (OpenAI, Anthropic, etc.), prompt engineering, agent orchestration frameworks, and practical applications of AI in software development workflows
Proven track record of architecting and building highly scalable distributed systems and developer‑facing platforms
Production experience with modern cloud‑native infrastructure including Kubernetes, GitOps deployment patterns, observability systems, and CI/CD pipelines
Proficiency in Go (preferred), Rust, Java, or Python with strong software engineering fundamentals
Experience designing developer tools, platform engineering systems, or internal tools that enable other teams
Exceptional product and platform mindset considering business outcomes, developer experience, and technical trade‑offs
Strong communication skills with ability to influence technical and non‑technical stakeholders across the organization
Track record of technical mentorship and elevating engineering teams’ capabilities
Ownership mentality with bias for action and iterative delivery in ambiguous, fast‑moving environments
Comfortable with autonomous work in distributed, remote‑first teams across multiple time zones
Preferred:
Experience with MCP (Model Context Protocol) or similar AI agent integration standards
Background in developer productivity, DevOps, SRE, or platform engineering domains
Contributions to open source AI tools, developer tooling, or platform engineering projects
Experience productizing internal platforms or tools into commercial offerings
Deep knowledge of security, compliance, and operational best practices for production AI systems
Experience with infrastructure‑as‑code frameworks (Terraform, Pulumi) and multi‑cloud platforms (AWS, GCP, Azure)
What to Expect First 30 Days
Understand Docker’s current AI and developer tooling landscape including Agent Dev project status, existing AI experiments, LLM provider relationships, and technical architecture decisions to date
Meet with engineering leadership, AI Developer Tools Senior Manager, and key technical stakeholders across product engineering to understand technical requirements, pain points, and architectural constraints
Conduct deep technical assessment of current developer tooling infrastructure (deployment, observability, security) to identify opportunities and constraints for AI tools platform
Review existing AI tool prototypes and internal experiments to understand what’s working, what isn’t, and technical lessons learned
Partner with Senior Manager to define initial technical priorities, architectural approach, and 90‑day technical roadmap
Establish relationships with platform teams (Infrastructure, Security, Data) to understand integration points and shared technical standards
First 90 Days
Define and document technical architecture for AI Developer Tools platform including system design, technology choices, integration patterns, and operational model
Ship first production AI developer tool with architectural patterns and technical standards that will scale to future tools (e.g., AI‑powered code review agent, deployment diagnostic agent, or on‑call assistance bot)
Establish technical foundations for self‑service platform including deployment pipeline, observability integration, security controls, and cost management mechanisms
Create comprehensive technical documentation, architectural decision records (ADRs), and best practices guides for teams building AI developer tools
Lead design reviews and technical decision‑making for team’s roadmap priorities
Mentor engineers on AI/LLM integration patterns, agent development techniques, and platform engineering practices
Define success metrics and instrumentation strategy for measuring AI tool adoption, effectiveness, and developer productivity impact
One Year Outlook
Establish mature technical architecture for AI Developer Tools with multiple production AI agents demonstrating value across Docker’s engineering organization
Build production‑ready self‑service platform enabling multiple teams to build, deploy, and operate their own AI tools with minimal friction
Define and implement technical standards for measuring AI tool effectiveness including adoption metrics, productivity improvements (commit frequency, PR velocity, deployment times, CI run times), and pipeline stability
Lead technical strategy for productizing successful internal AI tools into customer‑facing offerings including architecture, security model, and scalability approach
Position AI Developer Tools as technical center of excellence for AI in developer workflows with regular technical talks, demos, and knowledge sharing
Demonstrate sustained technical leadership impact through mentorship, architectural decisions, and elevation of engineering organization’s AI capabilities
Define multi‑year technical roadmap for AI Developer Tools including advanced agent capabilities, expanded platform features, and emerging AI/LLM technology adoption
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.
#LI-REMOTE
#J-18808-Ljbffr
Docker seeks
a Principal Engineer
to define the technical vision and architecture for our new AI Developer Tools team. This is a rare opportunity to establish the technical foundation for how AI will transform developer productivity—both internally at Docker and for our customers worldwide. You'll work at the intersection of AI, developer experience, and platform engineering, architecting cutting‑edge AI‑powered developer tools and building the platform that enables teams across Docker to rapidly prototype, deploy, and scale their own AI developer tools.
As Principal Engineer, you’ll own the technical strategy for two critical areas:
AI‑Powered Developer Tools Architecture:
Define the technical architecture for innovative AI agents and tools that accelerate developer productivity, provide observability insights, and automate operational reviews. Lead the design and implementation of tools that make it easier for teams to adopt an AI‑native mindset and accelerate adoption of AI developer tools such as Claude Code, Cursor, Warp, or other AI tools as they gain traction within Docker teams. You’ll architect 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.
Self‑Service AI Developer Tools Platform:
Design and build the foundational platform infrastructure that empowers product and platform teams across Docker to unblock themselves by rapidly prototyping, building, and deploying their own AI developer tools. Establish technical standards, architectural patterns, and best practices that enable teams to experiment with AI solutions for their unique pain points, iterate quickly on tool ideas, and graduate successful prototypes into production‑ready services.
As internal AI developer tools demonstrate value and gain traction, you’ll partner with product and engineering leadership to evaluate productization opportunities—defining the technical architecture for transforming proven internal tools into new commercial offerings for Docker’s customers.
Reporting to the Director of Platform Infrastructure & AI Developer Tools, you’ll collaborate closely with engineering leadership across Docker, the AI Developer Tools Senior Manager, product engineering teams, platform teams, and ultimately customers as internal tools evolve into product offerings.
What Would Make Someone Successful in This Role You’re a technical leader who excels at the intersection of AI, developer experience, and platform engineering. You have deep expertise in LLM integration, AI agents, and practical applications of AI in developer workflows, with a track record of shipping production AI systems at scale. You think in systems and platforms, designing architectures that enable leverage—building once and enabling dozens of teams to move faster. You understand the nuances of developer tooling and can articulate clear opinions on what makes tools that developers love to use. You have exceptional judgment on when to build custom solutions versus integrate existing tools, and you’re comfortable navigating the rapidly evolving AI/LLM landscape. You balance technical excellence with pragmatism, shipping iteratively while maintaining high quality bars. Most importantly, you lead through influence and mentorship, elevating the entire engineering organization's technical capabilities around AI and developer tooling.
Responsibilities Technical Leadership & Architecture
Define the long‑term technical vision and architecture for AI‑powered developer tools and the self‑service platform that enables teams to build their own AI agents
Establish architectural patterns, technical standards, and best practices for LLM integration, AI agent development, and production AI systems serving developers
Lead technical strategy for platform capabilities including deployment frameworks (ArgoCD/GitOps), observability integration (Grafana), security controls, and operational tooling for AI developer tools
Design highly available, scalable infrastructure for hosting AI agents and developer tools with predictable performance and intelligent resource management
Drive technical decisions on AI technology choices, LLM provider strategies, prompt engineering approaches, and agent orchestration frameworks
Partner with Senior Manager and product leadership to align technical architecture with business objectives and productization opportunities
Systems Design & Implementation
Architect and build production‑ready AI agents for developer productivity including code review assistants, test generators, deployment diagnostics, and incident response automation
Design and implement the self‑service platform infrastructure that reduces time‑to‑production for new AI tools from weeks to days
Build systems that accelerate adoption of AI‑native development tools (Claude Code, Cursor, Warp) across Docker’s engineering organization
Establish reliability, security, and performance standards for AI systems including SLOs, monitoring, incident response, and cost management
Design integration points between AI developer tools and existing developer infrastructure (CI/CD pipelines, observability platforms, deployment systems)
Lead technical implementation of AI tools that improve early‑stage development metrics (commits, PRs), deployment pipelines, and incident response while maintaining pipeline stability
Strategic Impact & Innovation
Evaluate emerging AI/LLM technologies, developer tooling trends, and agent frameworks to inform Docker’s technical strategy
Define technical approach for productizing internal AI developer tools into customer‑facing offerings
Drive technical standards for measuring AI tool effectiveness including adoption metrics, productivity gains, and developer satisfaction
Optimize AI tool performance, cost efficiency, and developer experience through architectural improvements and LLM provider strategies
Lead cross‑functional technical discussions influencing company‑wide AI and developer tooling architecture
Leadership & Mentorship
Mentor senior and staff engineers on AI/LLM integration patterns, agent development, and platform engineering best practices
Lead design reviews and technical decision‑making for production AI systems
Foster culture of technical excellence, experimentation, and rapid prototyping within AI Developer Tools team
Serve as primary technical contact and thought leader for AI in developer workflows across Docker’s engineering organization
Collaborate with platform teams (Infrastructure, Security, Data) to establish shared technical standards and integration patterns
Qualifications Required:
10+ years software engineering experience with 3+ years in Staff or Principal Engineer roles
Deep expertise in AI/ML technologies with hands‑on production experience building LLM‑powered applications, AI agents, or AI‑assisted developer tools
Strong understanding of LLM APIs (OpenAI, Anthropic, etc.), prompt engineering, agent orchestration frameworks, and practical applications of AI in software development workflows
Proven track record of architecting and building highly scalable distributed systems and developer‑facing platforms
Production experience with modern cloud‑native infrastructure including Kubernetes, GitOps deployment patterns, observability systems, and CI/CD pipelines
Proficiency in Go (preferred), Rust, Java, or Python with strong software engineering fundamentals
Experience designing developer tools, platform engineering systems, or internal tools that enable other teams
Exceptional product and platform mindset considering business outcomes, developer experience, and technical trade‑offs
Strong communication skills with ability to influence technical and non‑technical stakeholders across the organization
Track record of technical mentorship and elevating engineering teams’ capabilities
Ownership mentality with bias for action and iterative delivery in ambiguous, fast‑moving environments
Comfortable with autonomous work in distributed, remote‑first teams across multiple time zones
Preferred:
Experience with MCP (Model Context Protocol) or similar AI agent integration standards
Background in developer productivity, DevOps, SRE, or platform engineering domains
Contributions to open source AI tools, developer tooling, or platform engineering projects
Experience productizing internal platforms or tools into commercial offerings
Deep knowledge of security, compliance, and operational best practices for production AI systems
Experience with infrastructure‑as‑code frameworks (Terraform, Pulumi) and multi‑cloud platforms (AWS, GCP, Azure)
What to Expect First 30 Days
Understand Docker’s current AI and developer tooling landscape including Agent Dev project status, existing AI experiments, LLM provider relationships, and technical architecture decisions to date
Meet with engineering leadership, AI Developer Tools Senior Manager, and key technical stakeholders across product engineering to understand technical requirements, pain points, and architectural constraints
Conduct deep technical assessment of current developer tooling infrastructure (deployment, observability, security) to identify opportunities and constraints for AI tools platform
Review existing AI tool prototypes and internal experiments to understand what’s working, what isn’t, and technical lessons learned
Partner with Senior Manager to define initial technical priorities, architectural approach, and 90‑day technical roadmap
Establish relationships with platform teams (Infrastructure, Security, Data) to understand integration points and shared technical standards
First 90 Days
Define and document technical architecture for AI Developer Tools platform including system design, technology choices, integration patterns, and operational model
Ship first production AI developer tool with architectural patterns and technical standards that will scale to future tools (e.g., AI‑powered code review agent, deployment diagnostic agent, or on‑call assistance bot)
Establish technical foundations for self‑service platform including deployment pipeline, observability integration, security controls, and cost management mechanisms
Create comprehensive technical documentation, architectural decision records (ADRs), and best practices guides for teams building AI developer tools
Lead design reviews and technical decision‑making for team’s roadmap priorities
Mentor engineers on AI/LLM integration patterns, agent development techniques, and platform engineering practices
Define success metrics and instrumentation strategy for measuring AI tool adoption, effectiveness, and developer productivity impact
One Year Outlook
Establish mature technical architecture for AI Developer Tools with multiple production AI agents demonstrating value across Docker’s engineering organization
Build production‑ready self‑service platform enabling multiple teams to build, deploy, and operate their own AI tools with minimal friction
Define and implement technical standards for measuring AI tool effectiveness including adoption metrics, productivity improvements (commit frequency, PR velocity, deployment times, CI run times), and pipeline stability
Lead technical strategy for productizing successful internal AI tools into customer‑facing offerings including architecture, security model, and scalability approach
Position AI Developer Tools as technical center of excellence for AI in developer workflows with regular technical talks, demos, and knowledge sharing
Demonstrate sustained technical leadership impact through mentorship, architectural decisions, and elevation of engineering organization’s AI capabilities
Define multi‑year technical roadmap for AI Developer Tools including advanced agent capabilities, expanded platform features, and emerging AI/LLM technology adoption
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.
#LI-REMOTE
#J-18808-Ljbffr