Senior Engineering Manager, Kernel and Virt

DigitalOcean, LLC; Seattle (WA); Hybrid; USD 200,800 - 251,000; Full time

Benefits offered by this job

Equity compensation

Education reimbursement

Flexible time-off policy

Job summary

DigitalOcean, LLC is hiring a Senior Engineering Manager in Seattle to lead their Inference Orchestration team. Responsible for driving strategy, execution, and scaling of their Kubernetes-based AI infrastructure, the ideal candidate will have extensive experience in engineering leadership and AI workload orchestration.

This hybrid role demands passionate and skilled individuals ready to tackle the challenges of a rapidly evolving tech landscape while ensuring high-performance and reliability.

Qualifications

• Proven track record in managing high-performing teams.
• Deep expertise in Kubernetes and AI infrastructure.
• Experience with GPU architectures and system optimization.

Responsibilities

• Lead the Inference Orchestration team to drive strategy and execution.
• Collaborate with cross-functional teams to align goals.
• Ensure operational health and stability of services.

Skills

Kubernetes expertise

AI workload orchestration

Engineering leadership

Cost management

Security best practices

Job description

Senior Engineering Manager, Kernel and Virt

Seattle

Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast‑paced environment of a true industry disruptor, you’ll find your place here. We value winning together—while learning, having fun, and making a profound difference for the dreamers and builders in the world.

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.

What You’ll Do

• 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 (1,000+ nodes, 10,000+ 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 multi‑component deployments (e.g., prefill leaders, decode workers, KV routers) with multi‑level 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).

What You’ll Bring

• 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.

Compensation Range

• $200,800 - $251,000

*This is a hybrid role

JR: 2026-7649

Why You’ll Like Working for DigitalOcean

• We innovate with purpose. You’ll be a part of a cutting‑edge technology company with an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.
• We prioritize career development. At DO, you’ll do the best work of your career. You will work with some of the smartest and most interesting people in the industry. We are a high‑performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. We provide employees with reimbursement for relevant conferences, training, and education. All employees have access to LinkedIn Learning’s 10,000+ courses to support your continued growth and development.
• We care about your well‑being. Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to local employee meetups to flexible time‑off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.
• We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.
• DigitalOcean is an equal‑opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.