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 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.
- $200,800 - $251,000
*This is a hybrid role
JR: 2026-7649
#LI-Hybrid
- 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 their 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.
Application Limit: You may apply to a maximum of 3 positions within any 180-day period. This policy promotes better role-candidate matching and encourages thoughtful applications where your qualifications align most strongly.
Skills Required
- Proven track record of managing and growing high-performing engineering teams
- Deep expertise in Kubernetes at scale and AI infrastructure orchestration
- Strategic knowledge of GPU architectures, interconnects (NVLink), and hardware topology impact
- Experience balancing performance and cost using resource management techniques (e.g., Dominant Resource Fairness)
- Familiarity with container runtime internals, system isolation, and security contexts
- Strong understanding of modern LLM serving architectures and serving engines (e.g., vLLM, Triton, SGLang)
- Expertise in observability and SLOs for infrastructure and inference metrics (e.g., TTFT, TPOT)
- Experience preferably within distributed systems or infrastructure domains
DigitalOcean Compensation & Benefits Highlights
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Healthcare Strength — Health coverage includes medical, dental, vision, and mental-health support plus employer-paid life/AD&D/disability, with multiple plan options and above-average employer contributions. Offerings are described as market-leading and in some cases fully paid to keep premiums low.
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Equity Value & Accessibility — Equity awards (new-hire and performance RSUs) are paired with an Employee Stock Purchase Plan offered at a discount. This ownership component augments cash compensation and broadens participation in company growth.
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Parental & Family Support — Paid parental leave is provided for a defined period and includes a structured, part-time transition-back program. The approach emphasizes a smoother return-to-work experience for new parents.
DigitalOcean Insights
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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Remote Workspace
Employees work remotely.
We commit to both remote work and in-person collaboration. These ways of working are dependent on specific roles and are mutually agreed upon by employees. In the US, we are mainly remote. In our APAC locations, we have a hybrid in-office approach.
