Staff Software Engineer, Docker Agents (London)
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!
Join Docker's AI engineering team to build the future of containerized AI agents. Docker containers are the perfect vehicle to host and run AI agents - providing isolation, portability, and reproducibility.
You'll be working on cagent, our open-source project (https://github.com/docker/cagent), and expanding on it to enable developers to build, deploy, and scale intelligent agents using Docker's container technology.
This is a greenfield opportunity to shape how developers leverage containers for AI agents at massive scale. You'll work alongside a team of seasoned engineers, collaborating with our sister teams working on Ask Gordon and Docker's broader AI platform. This is a unique opportunity to combine cutting-edge AI techniques with container technology to solve real-world developer problems.
Please note: this role is only available to candidates currently located in London (or reasonable commuting distance to London). We are unable to make exceptions to this location requirement.
Responsibilities
- As a Staff Engineer, you will partner with the engineering leadership to help set technical direction and serve as a guide and mentor as the team grows and matures
- Build Containerized Agent Systems: Design and implement systems that leverage Docker containers as the ideal runtime for AI agents, ensuring isolation, scalability, and portability
- Expand cagent: Maintain and evolve the open-source cagent project, adding new capabilities for containerized agent deployment and orchestration
- Agent Runtime Development: Build robust infrastructure for packaging, deploying, and managing agents in containers
- RAG Integration: Enhance agent capabilities with Retrieval-Augmented Generation systems to provide contextual knowledge and domain expertise (secondary focus)
- Evaluation & Testing: Design robust evaluation frameworks to measure agent performance, reliability, and containerized deployment effectiveness
- Rapid Prototyping: Iterate quickly on new agent capabilities and deployment patterns, moving from concept to production efficiently
- Open Source Community: Engage with the cagent community, review contributions, and help grow the ecosystem
- Cross-functional Collaboration: Work closely with product managers, designers, and engineers across Docker's AI teams to integrate containerized agent capabilities into Docker's developer experience
- On-Call Rotation: Take part in on-call rotation for your team; respond to incidents, debug production issues, and drive continuous improvement of system reliability
What to expect
First 30 days
- Integrate into our dynamic AI engineering team building containerized agent infrastructure
- Deep dive into cagent's architecture and our containerized agent deployment roadmap
- Contribute your first enhancements to cagent with the help of your team
- Understand our technical stack and begin collaborating with sister AI teams
First 90 days
- Lead significant features or improvements to cagent and our containerized agent platform
- Enhance agent deployment capabilities and container-based orchestration patterns
- Collaborate with the open-source community on cagent development
- Help other new team members onboard
- Regularly interact with internal stakeholders and analyze user feedback
One Year Outlook
- Drive major architectural decisions for our containerized agent platform that will impact millions of Docker users
- Continue to help grow the team and develop efficient agent development processes
- Contribute to evaluation frameworks and performance optimization across our agent systems
- Lead initiatives to expand containerized agent capabilities for enterprise use cases
- Grow your skills in enterprise-grade containerized AI system architecture and deployment
- Shape the future direction of cagent and Docker's agent ecosystem
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
Qualifications
- Go Expertise: Strong, demonstrated, professional proficiency in Go (this is absolutely required) - Docker's primary language for backend systems
- AI/ML Knowledge: Practical experience with large language models (LLMs) and agent development
- System Architecture: Proven ability to design scalable, distributed systems
- Container Technology: Deep understanding of Docker, containerization best practices, and container orchestration
- RAG Systems: Experience building Retrieval-Augmented Generation systems (secondary focus)
- Rapid Iteration: Demonstrated ability to prototype quickly and iterate based on feedback
- AI Frameworks: Experience with CrewAI, AGNO, ADK, LangChain/LangGraph or similar AI orchestration frameworks (peferred)
- Python Proficiency: Experience with Python for AI prototyping and tooling (prefered)
- Experience with Kubernetes or container orchestration platforms (preferred)
- Open source contributions and community engagement (preferred)
- Experience with agent evaluation, reliability, and observability techniques (preferred)
- 8+ years of directly applicable experience
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience
