Job description

ABOUT DOCKER Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout. We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default. _______________________________________________________________________ Docker AI Gateway is our answer to the complexity of taking AI agents from prototype to production. It’s a powerful, intelligent, and secure control point that eliminates the toil of model orchestration, tool management, observability, and governance - so developers can focus on building incredible AI agents, not gluing together infrastructure. The Gateway sits at the center of modern AI applications, offering: - A model and tool routing layer with built-in security and cost optimization - A familiar OpenAI-compatible interface and MCP server - Unified observability and policy enforcement - Auto-RAG, tool injection, session summarization, and more We’re just getting started and we need exceptional engineers to help us build the backbone of the future of agent-based development. WHAT YOU’LL DO - Set technical direction for core systems powering the AI Gateway 0 the model router, MCP gateway, and control plane - and make sure architectural decisions hold up as usage scales by 10x - Consistently identify gaps in our infrastructure, process, or tooling before they become blockers, and drive the fix from idea to production - Take a significant, hands-on role in defining, delivering, and implementing the Gateway's technical strategy, in close partnership with engineering leadership - Own the hardest, highest-ambiguity problems in the system: dynamic model selection, auto-failover, cost-based routing, and policy enforcement that has to work end-to-end - Act as the go-to expert for secure credential storage, session summarization, caching, and rate limiting - the capabilities everything else depends on - Build the infrastructure that lets us, and our customers, evaluate, replay, and backtest agent and LLM behavior with confidence - Mentor senior and mid-level engineers, and raise the technical bar for the team through code, design review, and example - Partner with product, design, and leadership to shape not just what the control plane UI does, but where the product goes next KEY PROBLEMS YOU’LL HELP SOLVE - Define the unified abstraction layer across diverse model and tool providers (OpenAI, Anthropic, Google, AWS Bedrock) that the rest of the org builds on - Architect secure, scalable identity and credential vaulting for tool and model access, trusted enough to run at enterprise scale - Build the infrastructure for real-time and historical analytics of AI agent behavior that leadership and customers both rely on - Guarantee that policy enforcement and logging work end-to-end - from prompt to tool to response - with no gaps an auditor (or attacker) could find - Set the bar for developer experience through intuitive APIs and first-class observability WHO YOU ARE Required Qualifications - 8+ years of backend engineering experience, with a track record of owning production-grade systems at significant scale - Deep specialization in distributed systems, cloud-native infrastructure, and API design - recognized by peers and cross-functional partners as a go-to expert, not just experienced - Experience building secure, high-throughput services (gateways, proxies, load balancers, policy engines), and a history of making judgment calls other engineers rely on - Fluency in Go and/or Rust - A track record of identifying systemic gaps - in architecture, process, or tooling-— and driving the fix across team boundaries, not just within your own - A strong product mindset, with real experience shaping technical direction rather than only executing against it - Comfortable operating with wide latitude: given a problem and significant autonomy in how to solve it, including when and who to pull in - Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience BONUS QUALIFICATIONS - Prior experience with OpenAI, Anthropic, or similar LLM APIs - Familiarity with RAG architectures, vector databases, or agent frameworks (e.g., LangChain, AutoGPT, CrewAI) - Experience with policy engines (e.g., OPA), observability frameworks (e.g., OpenTelemetry), or API gateways - Understanding of OAuth 2.1, secret management, and cloud IAM systems - Experience with Kubernetes, Docker, and microservices architecture - A track record of setting technical standards that other teams or engineers went on to adopt WHY JOIN US? - Work on what’s next: The agent era is just beginning. Help define the infrastructure stack for the next generation of software. - Own mission-critical infrastructure: Be part of a small team building the backbone of secure, intelligent agent applications. - Impact at scale: Your work will touch tens of thousands of developers building AI-first applications. - Engineering excellence: Join a world-class engineering team that values simplicity, code quality, and deep technical thinking. - Remote-first culture: Work from anywhere with a team that trusts and empowers you. Docker considers sponsorship on a case-by-case basis based on business needs. Compensation & Equity United States: $170,350 – $275,550 + equity ______________________________________________________________________ POSTING INFORMATION - Open vacancy: This posting is for an existing open role. - AI in hiring: Docker may use AI-assisted tools during our recruiting process. - 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. ______________________________________________________________________ PERKS & BENEFITS - Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration. - Flexibility that fits your life – We trust you to manage your schedule while delivering great work. - Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break. - Home office support – Set up your workspace for comfort and success. - Technology stipend – Equivalent to US$100 net per month to help support your work. - Learning & development – Annual stipend for conferences, courses, certifications, and continued learning. - Parental leave – 16 weeks of paid parental leave after six months of employment. - Equity for all full-time employees – Share in Docker's long-term success as we continue to grow. - Comprehensive benefits – Medical, retirement, and paid holidays vary by country. - Docker swag – Because representing the whale never gets old. Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community. #LI-REMOTE

Requirements

Must-haves

Set technical direction for core systems powering the AI Gateway 0 the model router, MCP gateway, and control plane - and make sure architectural decisions hold up as usage scales by 10x
Consistently identify gaps in our infrastructure, process, or tooling before they become blockers, and drive the fix from idea to production
Take a significant, hands-on role in defining, delivering, and implementing the Gateway's technical strategy, in close partnership with engineering leadership
Own the hardest, highest-ambiguity problems in the system: dynamic model selection, auto-failover, cost-based routing, and policy enforcement that has to work end-to-end
Act as the go-to expert for secure credential storage, session summarization, caching, and rate limiting - the capabilities everything else depends on
Build the infrastructure that lets us, and our customers, evaluate, replay, and backtest agent and LLM behavior with confidence
Mentor senior and mid-level engineers, and raise the technical bar for the team through code, design review, and example
Partner with product, design, and leadership to shape not just what the control plane UI does, but where the product goes next
Define the unified abstraction layer across diverse model and tool providers (OpenAI, Anthropic, Google, AWS Bedrock) that the rest of the org builds on
Architect secure, scalable identity and credential vaulting for tool and model access, trusted enough to run at enterprise scale
Build the infrastructure for real-time and historical analytics of AI agent behavior that leadership and customers both rely on
Guarantee that policy enforcement and logging work end-to-end - from prompt to tool to response - with no gaps an auditor (or attacker) could find
Set the bar for developer experience through intuitive APIs and first-class observability
8+ years of backend engineering experience, with a track record of owning production-grade systems at significant scale
Deep specialization in distributed systems, cloud-native infrastructure, and API design - recognized by peers and cross-functional partners as a go-to expert, not just experienced
Experience building secure, high-throughput services (gateways, proxies, load balancers, policy engines), and a history of making judgment calls other engineers rely on
Fluency in Go and/or Rust
A track record of identifying systemic gaps - in architecture, process, or tooling-— and driving the fix across team boundaries, not just within your own
A strong product mindset, with real experience shaping technical direction rather than only executing against it
Comfortable operating with wide latitude: given a problem and significant autonomy in how to solve it, including when and who to pull in
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
Prior experience with OpenAI, Anthropic, or similar LLM APIs
Familiarity with RAG architectures, vector databases, or agent frameworks (e.g., LangChain, AutoGPT, CrewAI)
Experience with policy engines (e.g., OPA), observability frameworks (e.g., OpenTelemetry), or API gateways

Nice-to-haves

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Keywords to mirror

dockeraigollmawsrestapirustkubernetes

Platform: ashby
Effort: quick

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Parental Leave