Senior Engineering Manager Kernel and Virt

We are seeking a Senior Engineering Manager to lead our Inference Orchestration team driving the strategy execution and scaling of our Kubernetes-based AI infrastructure. You will be responsible for balancing business needs with technical excellence ensuring high throughput optimal GPU utilization and robust fault tolerance for our next-generation disaggregated inference fine-tuning and training workloads.

• Team Leadership & Development: Recruit mentor and coach engineers on the team fostering a culture of ownership technical excellence and continuous improvement.
• Execution & Delivery: Own the team's project execution translating high-level business goals into clear technical roadmaps measurable milestones and successful on-time delivery.
• Cross-Functional Partnership: Collaborate with Product Management other engineering teams and key stakeholders to align priorities manage dependencies and communicate progress and risks.
• Operational Health: Ensure the production health stability and on-call rotation of all services owned by the Inference Orchestration team.
• Strategic Architecture & Planning: Define the technical roadmap and oversee the architecture of high-throughput scheduling systems for massive Kubernetes clusters (1000+ nodes 10000+ pods) focusing on scalability techniques like multi-scheduler architectures and batch dispatching.
• Maximize GPU Utilization: Eliminate GPU waste in multi-tenant environments by implementing fractional GPU allocation leveraging mechanisms like KAI-Scheduler's Reservation Pods or hard-isolation tools like HAMi and configuring time-based fairshare scheduling to balance over-quota pool access.
• Orchestrate Complex Inference: Implement and manage disaggregated AI inference pipelines using frameworks like NVIDIA Grove coordinating multicomponent deployments (e.g. prefill leaders decode workers KV routers) with multilevel autoscaling and explicit startup ordering.
• Optimize Placement & Topology: Deploy topology-aware scheduling to align pod placement with physical hardware dimensions such as NVLink connections PCIe lanes and NUMA nodes minimizing communication latency for multi-GPU operations.
• Platform Performance & Reliability: Drive initiatives to enhance overall cluster performance including optimizing scheduling latency API server load and implementing fault tolerance mechanisms like Checkpoint/Restore for long-running AI training jobs.
• Manage AI Storage & Fault Tolerance: Orchestrate efficient model weight distribution using OCI Image Volumes and implement Checkpoint/Restore capabilities (via CRIU and NVIDIA cuda-checkpoint) for long-running training fault recovery.
• Security and Isolation: Define and enforce security best practices for AI workloads ensuring multi-layered isolation environments and agent sandboxes are deployed to safely execute untrusted code (e.g. using Kata Containers gVisor or microVMs).

• Engineering Leadership Experience: Proven track record of managing and growing high-performing engineering teams preferably within a distributed systems or infrastructure domain.
• Kubernetes and AI Infrastructure Domain Knowledge: Deep expertise in Kubernetes at scale and a strong foundational understanding of the core challenges in AI workload orchestration scheduling and resource management.
• Hardware-Aware Optimization: Strategic knowledge of GPU architectures (NVIDIA and/or AMD) interconnects (like NVLink) and hardware topology and their direct impact on AI training and inference performance.
• Resource and Cost Management: Experience in balancing performance against cost applying principles like Dominant Resource Fairness (DRF) and directing strategies for maximizing cluster efficiency.
• Systems Engineering & Security: Familiarity with concepts in container runtime internals system isolation and security contexts to manage risk in shared infrastructure.
• AI/ML Serving Architectures: Strong understanding of modern LLM serving architectures disaggregation patterns and common serving engines (e.g. vLLM Triton SGLang).
• Observability and SLOs: Expertise in defining tracking and operationalizing deep infrastructure and inference metrics (e.g. TTFT TPOT) to drive performance improvements and meet service level objectives.

• $200800 - $251000

*This is a hybrid role

JR: 2026-7649

#LI-Hybrid

What We Do

DigitalOcean is the Inference Cloud — a full-stack production-ready cloud platform built to run AI applications with predictable performance sustainable economics and radically simpler operations at scale. We are built for teams turning AI into real products — not just training models. Our advantage is not fewer features but fewer failure modes when operating AI at scale — combining minimal operational overhead predictable cost efficiency and a full-stack cloud that works as a system. Hyperscalers are broad by design. Neoclouds are infrastructure-first. DigitalOcean is inference-first — with a real cloud underneath. It combines inference-optimized compute managed inference software and integrated cloud capabilities that reduce operational burden for teams running real workloads. Inference is the foundation—not the boundary. Everything else builds on top of it.

Why Work With Us

At DO we do career-defining work. We innovate with AI and build cutting-edge tech. Our rewards to match that intensity - to motivate you recognize your impact and give you what you need to thrive. If you have a growth mindset like to think big and bold and are energized by the fast-paced environment you'll find your place here.

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