Docker - Staff Software Engineer, AI Developer Tools

Remote - San Francisco, California, United States$170k - $276k+ Equity2w ago

Remote Staff NA Diagnostics Cloud Computing Staff Engineer Software Engineer Go Rust Java Python Team Leadership

Requirements

• 8+ years of software engineering experience, with demonstrated technical leadership on production systems at scale • Hands-on production experience building LLM-powered applications, AI agents, or AI-assisted developer tools • Strong understanding of LLM APIs (OpenAI, Anthropic, and others), prompt engineering, agent orchestration frameworks, and practical uses of AI in software development workflows • Track record of designing and building scalable distributed systems and developer-facing platforms • Production experience with 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 systems, or internal tools that other teams rely on • Track record of clarifying requirements and constraints with stakeholders before committing to a design • Track record of leading complex initiatives that crossed team boundaries, and of influencing without direct authority • Strong product and platform judgment: you weigh business outcomes, developer experience, and technical trade-offs • Clear written and spoken communication suited to a remote, async-first company • Track record of mentoring other experienced engineers • Ownership mentality with a bias for action and iterative delivery in ambiguous, fast-moving environments • Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience • Preferred • Experience with MCP (Model Context Protocol) or similar AI agent integration standards • Background in developer productivity, DevOps, SRE, or platform engineering • Contributions to open source AI tools, developer tooling, or platform engineering projects • Experience evolving an internal platform or tool into a widely adopted, production-grade service • Knowledge of security, compliance, and operational practices for production AI systems • Experience with infrastructure-as-code (Terraform, Pulumi) and major cloud platforms (AWS, GCP, Azure) • What to Expect • First 30 Days • Build context on Docker's current AI and developer tooling landscape, including existing AI experiments, LLM provider relationships, and architecture decisions to date • Meet the Senior Manager, the team's Principal engineers, engineering leadership, and key stakeholders in product engineering to understand needs, pain points, and constraints • Review existing AI tool prototypes and internal experiments to learn what is working and what is not • Get hands-on with the team's services and ship a useful production change • Shadow on-call and learn the operational profile of the team's services • Work with the Senior Manager to agree on initial technical priorities • First 90 Days • Lead the technical framing and delivery plan for one significant problem, from requirements through to production • Document the architecture, technology choices, and integration patterns for that work, including ADRs • Ship a production AI developer tool that sets patterns the team can reuse (for example an AI-powered code review agent, a deployment diagnostic agent, or an on-call assistant) • Contribute to the technical foundations of the self-service platform: deployment pipeline, observability integration, security controls, and cost management • Lead design reviews for your area of the roadmap and begin mentoring other engineers • Take part in the on-call rotation • First Year Outlook • Be the trusted technical lead for your area of AI Developer Tools, and a sought-after partner for teams across Docker • Deliver at least one material outcome that spans multiple teams or systems • Have multiple production AI tools demonstrating value across Docker's engineering organization • Help build a self-service platform that lets multiple teams build, deploy, and operate their own AI tools with little friction • Define and instrument how the team measures AI tool adoption, effectiveness, and developer productivity impact • Grow other senior engineers into broader ownership, and reduce the number of decisions that depend on a small set of people • Docker considers sponsorship on a case-by-case basis based on business needs.

Responsibilities

• Technical Leadership and Architecture • Set technical direction and own the architecture for AI-powered developer tools and the self-service platform within the team's roadmap • Turn ambiguous problems into clear requirements, plans, and staged decisions. Ask about scale, users, and constraints before designing, and write down your assumptions • Establish patterns, standards, and best practices for LLM integration, agent development, and production AI systems that the team and partner teams adopt • Shape platform capabilities including deployment frameworks (ArgoCD/GitOps), observability integration (Grafana), security controls, and operational tooling for AI developer tools • Design reliable, scalable infrastructure for hosting AI agents and developer tools • Recommend AI technology choices, LLM provider strategies, and agent orchestration approaches, and make the trade-offs explicit • Work with the Senior Manager and the team's Principal engineers to align technical decisions with business goals • Systems Design and Implementation • Design, build, and operate production AI agents for developer productivity, including code review assistants, test generators, deployment diagnostics, and incident response automation • Build self-service platform capabilities that reduce time-to-production for new AI tools from weeks to days • Build systems that speed up adoption of AI-native development tools (Claude Code, Codex) across Docker's engineering organization • Set and meet reliability, security, and performance standards for AI systems, including SLOs, monitoring, incident response, and cost management • Design integrations between AI developer tools and existing developer infrastructure such as CI/CD pipelines, observability platforms, and deployment systems • Take part in the team's on-call rotation and use what you learn to improve alerts, runbooks, and tooling • Strategic Impact and Innovation • Evaluate emerging AI/LLM technologies, developer tooling trends, and agent frameworks, and recommend what Docker should adopt and what it should not build • Define how we measure AI tool effectiveness, including adoption, productivity gains, and developer satisfaction • Improve AI tool performance, cost efficiency, and developer experience through architectural improvements • Contribute to the technical approach for hardening proven internal tools so other teams across Docker can adopt them • Lead cross-team technical delivery, surface risks early, and work with Infrastructure, Security, and Data on shared standards • Leadership and Mentorship • Mentor senior engineers on AI/LLM integration patterns, agent development, and platform engineering practices • Lead design reviews and raise the quality of design and code reviews across the team • Create reusable patterns, documentation, and architectural decision records (ADRs) that other engineers build on • Help the team turn good individual AI-assisted engineering practice into shared workflows and controls • This role may require participation in an on-call rotation to provide support outside of standard business hours, including evenings, weekends, and holidays, as needed.

Benefits

• United States: $170,350 – $275,550 + equity • United States: • ______________________________________________________________________ • Open vacancy: This posting is for an existing open role. • Open vacancy: • AI in hiring: Docker may use AI-assisted tools during our recruiting process. • AI in hiring: • Interview recordings: Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent. • Interview recordings: • Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration. • Remote-first by design • Flexibility that fits your life – We trust you to manage your schedule while delivering great work. • Flexibility that fits your life • Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break. • Time to recharge • Technology stipend – Equivalent to US$100 net per month to help support your work. • Technology stipend • US$100 net per month • Learning & development – Annual stipend for conferences, courses, certifications, and continued learning. • Learning & development • Parental leave – 16 weeks of paid parental leave after six months of employment. • Parental leave • Equity for all full-time employees – Share in Docker's long-term success as we continue to grow. • Equity for all full-time employees • Comprehensive benefits – Medical, retirement, and paid holidays vary by country. • Docker swag – Because representing the whale never gets old. • Docker swag