We are looking for a Software Development Engineer II (SDE-2) to join the EKS Runtime Release team. In this role, you will design, build, and operate systems that power the compute layer for Amazon EKS, working on critical infrastructure that enables customers to run containerized workloads reliably and securely at scale. You will own the end-to-end AMI lifecycle including designing optimized Amazon Machine Images for EKS workloads across Amazon Linux distributions, implementing automated build and release pipelines with integration testing, and ensuring compliance with 21-day CVE patching SLAs through automated tooling. Your work will involve qualifying and certifying new GPU and accelerator instance types such as P6 (B200/B300), G7, and Trainium2, managing NVIDIA driver updates and multi-version support strategies, and integrating Dynamic Resource Allocation (DRA) drivers for GPU, EFA, and Neuron workloads. You will develop and operate the Node Monitoring Agent that runs as a DaemonSet on customer nodes, implementing health checks, automated log collection, and EFA monitoring capabilities to provide fleet-wide observability. A significant portion of your work will focus on enabling AI and ML workloads by implementing VM isolation runtimes with Nitro partition support for secure container startup, optimizing node startup sequences for Auto Mode, and building support for AI agent workload patterns including pod pause, resume, snapshot, and restore capabilities. You will also update and maintain container runtimes including containerd and runc, configure SOCI snapshotters for improved cold-start performance, implement advanced kubelet configurations for huge pages and CPU topology management, and ensure GPU Operator compatibility across the fleet. As an SDE-2, you will drive technical design decisions for node runtime architecture, collaborate with service teams across AWS to integrate new capabilities, provide 24/7 oncall coverage for operational support, respond to SEVs and customer escalations, and mentor junior engineers while raising the bar on engineering and operational excellence. This is an opportunity to work on foundational infrastructure that directly impacts millions of customers running containerized workloads on AWS, with particular focus on enabling the next generation of GPU-accelerated AI and ML applications.
Key job responsibilities
Design & Build: Architect and implement the node-level runtime infrastructure that powers EKS compute. Design optimized Amazon Machine Images (AMIs), integrate GPU drivers and accelerator support, implement VM isolation runtimes, and build node monitoring systems that operate reliably across millions of customer nodes.
Operate at Scale: Own the operational health of the EKS runtime layer handling millions of customer workloads across diverse instance types and accelerators. Participate in on-call rotations, respond to SEVs, resolve customer escalations, and drive operational improvements that reduce MTTR and improve fleet stability.
Technical Leadership: Lead the design and implementation of complex node runtime features end-to-end, from requirements through AMI build pipelines, testing frameworks, deployment, and production validation. Drive technical decisions on AMI architecture, container runtime strategies, and GPU/accelerator integration.
Kubernetes & Runtime Expertise: Work deeply with node-level Kubernetes components including kubelet configuration, device plugins, Dynamic Resource Allocation (DRA) drivers, and container runtimes (containerd, runc). Understand Linux internals, systemd, GPU drivers, and isolation technologies to build robust node capabilities.
Cross-Team Collaboration: Partner with EKS control plane teams, EC2 instance teams, NVIDIA, AWS AI/ML services (SageMaker, Trainium, Inferentia), and open-source communities to deliver integrated solutions that enable customer workloads from traditional containers to cutting edge AI training and inference.
Mentorship: Mentor junior engineers on systems programming, Linux internals, and operational best practices. Conduct thorough code reviews, share knowledge on GPU technologies and container runtimes, and contribute to a culture of engineering excellence.
Operational Excellence: Drive improvements in AMI build and release pipelines, automated CVE patching, instance qualification testing, monitoring and alarming for node health, and incident response processes. Build automation that reduces manual toil and accelerates time-to-resolution.
Innovation: Identify opportunities to enable new GPU and accelerator technologies, optimize node startup performance, improve container isolation and security, and push the boundaries of what's possible with AI/ML workloads on Kubernetes. Simplify customer experiences through better defaults, comprehensive monitoring, and self-healing capabilities.
About the team
The EKS Runtime Release team builds and operates the foundational compute layer that powers Amazon Elastic Kubernetes Service (EKS). We own the node-level infrastructure that makes EC2 instances become reliable, secure, and high-performance Kubernetes nodes. This includes designing and building optimized Amazon Machine Images (AMIs), integrating GPU drivers and accelerator support, implementing container runtimes and isolation technologies, building node monitoring systems, and automating security patching across the fleet. Our systems run on millions of customer nodes and enable workloads ranging from traditional microservices to the largest AI training and inference clusters in the world.
Our team tackles challenges at the intersection of Linux systems programming, Kubernetes internals, GPU/accelerator technologies, and distributed systems operations at massive scale. We're building the next generation of node capabilities—from VM-based pod isolation with Nitro partitions to Dynamic Resource Allocation for GPUs and accelerators, from qualifying cutting-edge instance types like P6 (B200/B300) and Trainium2 to enabling AI agent workload patterns with pod pause, resume, and snapshot capabilities. We work closely with NVIDIA, EC2 instance teams, AWS AI/ML services, and the Kubernetes community to deliver the most capable and reliable node platform for containerized workloads.
Inclusive Team Culture
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences. Amazon's culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
Work/Life Balance
Our team puts a high value on work-life balance. It isn't about how many hours you spend at home or at work; it's about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. While we maintain 24/7 on-call coverage to support our critical infrastructure, we distribute the operational load fairly and invest heavily in automation and operational excellence to minimize toil and ensure sustainable on-call burden.
Mentorship & Career Growth
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge sharing and mentorship. Whether you're learning about Linux kernel internals, GPU driver architecture, Kubernetes device plugins, or distributed systems operations, our senior members provide one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer - balancing innovation work (new GPU support, AI workload patterns) with operational excellence (automation, monitoring, reliability improvements)—and enable them to take on more complex technical leadership in the future.